DEV Community: Anurag Singh

You Have One Identity. Your Security Stack Sees Seven.

Anurag Singh — Tue, 08 Sep 2026 10:05:50 +0000

I was mapping out access for a mid-sized engineering org a while back, and we hit a wall almost immediately: nobody could tell us, with confidence, how many accounts one senior engineer actually had.

By the time we finished counting, the answer was seven. Active Directory account. Okta identity. An AWS IAM role. A GitHub account with org-admin on two repos. Microsoft 365 access. A VPN identity. A privileged role in a cloud console nobody remembered granting.

Seven accounts. One person. And not one system in that stack had the full picture.

That's not a rare setup. It's basically the default now, especially for anyone touching infrastructure, CI/CD, or cloud resources regularly. And it creates a strange asymmetry: for your SOC, that's seven disconnected pieces of data sitting in seven different consoles. For an attacker who compromises just one of those seven, it's one identity with several paths into the org, and they only need to find the weakest one.

The Identity You Can See Isn't the Identity You Actually Have

Identity data is scattered by design. Directories, identity providers, cloud platforms, SaaS apps, HR systems, PAM tools, VPNs, databases — each one knows a slice of the truth about an account, and almost none of them know the whole story about the person or entity behind it.

Here's a scenario worth sitting with. A developer suddenly accesses a repository they've never touched. Around the same time, that same person authenticates from an unfamiliar location, picks up a new privilege, and reaches a cloud resource they don't normally go near.

Any one of those, alone, has a boring explanation. New project, new laptop, new access request that hasn't been revoked yet. But stacked together, on the same identity, in a short window, that combination starts looking a lot less boring. The hard part was never really detecting each event individually. Most tools can do that. The hard part is realizing all four events belong to the same person, and understanding what that person can actually reach if they've been compromised.

Accounts Aren't the Same Thing as Identities

Traditional identity monitoring hands security teams siloed views almost by default. Active Directory shows one account. Okta shows another. AWS shows a third. Every SaaS app you add stacks another account and another set of permissions on top.

Which creates a pretty basic mismatch: the security team sees accounts. What they actually need to understand is identities. That gap matters more as an org scales, because a dormant account can quietly become active, a privileged identity can start behaving differently, a service account can touch an application it's never touched before, and permissions can pile up on someone's profile for years without anyone reviewing why they're still there. Without something correlating those accounts back to one entity, all of that stays scattered across dashboards nobody's cross-referencing in real time.

Risk Isn't a Single Login Event

Not every identity carries the same weight. A standard employee account behaving exactly like it always does is a non-event. A privileged admin account suddenly reaching for something unfamiliar is not. A dormant account waking up isn't the same story as a service account running its normal, scheduled workload.

Treating all of these the same way, as isolated login events, is part of why alert fatigue exists in the first place. What actually matters is a continuously updated read on which identities are becoming risky right now, not a static list of who technically has an account.

Knowing an Identity Is Risky Isn't the Same as Knowing What It Can Reach

This is the part that's easy to skip past. A privileged identity connected to a dozen applications, several groups, and multiple cloud resources represents a very different level of exposure than a similarly "risky-looking" identity that's isolated to one low-value system. If either one gets compromised, the actual damage depends entirely on its access paths, not just on the fact that something looked unusual.

Being able to visualize that, what applications an identity touches, which groups it belongs to, what privileged access it holds, and what the realistic blast radius looks like if it's compromised, turns "this account triggered an alert" into something an analyst can actually act on.

Detection Is Only Half the Problem

Finding a risky identity is the easier half. The harder question is what happens in the minutes right after that.

If every high-risk identity requires a manual ticket, a handoff to another team, and three rounds of approval before anything happens, your response time is going to lose to an attacker who's already moving. This is exactly the kind of decision that benefits from being pre-approved and automated for high-confidence cases: lock the account, force MFA re-enrollment, clean up a dormant identity, contain a privileged account that's acting outside its normal pattern. Not to cut humans out of the loop, but to cut the unnecessary waiting out of the cases where the evidence is already clear.

Identity Attacks Rarely Look Like Malware

One of the more frustrating things about identity-based attacks is that they often skip the part your endpoint tools are built to catch. There's no malicious file, no obvious exploit. Just a valid credential, used in a way that doesn't quite match how it's normally used: impossible travel, an MFA bypass, credential stuffing, a privileged identity reaching somewhere it's never gone before.

Every one of those can look completely legitimate to a control that's only checking "is this credential valid." The behavior around the credential is usually where the real story is, which is exactly why this has to be a correlation problem, not a single-signal detection problem.

This Gets Worse, Not Better, at MSSP Scale

For a single enterprise, identity fragmentation might mean juggling a dozen systems. For an MSSP managing multiple customers, that same fragmentation exists per tenant, and it multiplies. Different identity providers, different directories, different cloud environments, different policies, times however many customers you're responsible for. Without a centralized way to score and monitor identity risk across tenants, analysts end up manually switching between disconnected consoles for every single customer, which doesn't scale past a handful of accounts.

Where I've Found This Actually Gets Solved

I'll be upfront: I work with Seceon. What convinced me this was worth writing about wasn't a feature list, it was watching how their aiSecurity UIDGuard360 handles the seven-accounts-one-person problem specifically. It resolves fragmented accounts across directories, cloud, HR, and SaaS sources into a single canonical identity, and scores risk continuously across credential, privilege, activity, access, behavioral, and compliance signals instead of treating every login as its own isolated event.

That's the shift that actually matters here. Not another dashboard, another data source connected to the pile. The difference between an analyst starting an investigation with "this account generated an alert" versus starting with "this person has these seven accounts, these privileges, and this level of risk right now" is enormous, and it's the difference that decides whether an investigation takes twenty minutes or two hours.

The Real Shift

Identity security has spent years focused on authentication, MFA, and access reviews. Those still matter. But modern identity attacks don't stop at the login screen. An attacker compromises an identity, reaches an application, touches cloud infrastructure, picks up a privileged role, and starts moving toward something valuable. The identity is the thread connecting every one of those steps.

Which means the real question was never just "how many identities do we have." It's closer to: who does this identity actually belong to, what can it reach, how does its behavior compare to normal, and what happens the moment something about it changes.

That's a harder problem than counting accounts. It's also the one that actually matters.

A Human Attacker Used AI Agents to Run a 10-Hour Intrusion. Here's What That Actually Means for Your Pipeline.

Anurag Singh — Mon, 07 Sep 2026 11:14:15 +0000

The most unsettling number in cybersecurity right now might be 10 hours.

Not 10 days. Not 10 weeks. 10 hours.

In a recent Unit 42 investigation, researchers responded to an enterprise intrusion where a human threat actor used frontier AI models and attack-specific agentic frameworks to automate large parts of the attack. The result: more than 50 MITRE ATT&CK techniques compressed into less than 10 hours, work that would normally take a human operator roughly two weeks.

The attack didn't stop at getting a foothold. The AI agents mapped the internal environment, searched source repositories, obtained root credentials, triggered unauthorized CI/CD activity, and targeted cloud AI infrastructure.

If you work anywhere near a CI/CD pipeline, a source repo, or a cloud deployment key, that list should sound uncomfortably familiar. This wasn't an attack on some abstract "enterprise network." It moved through the exact systems most engineering teams touch every day.

Attacks Used to Be Sequential. Now They're Parallel.

Defenders have long thought about intrusions as a chain: initial access, discovery, credential access, lateral movement, persistence, exfiltration. The steps were never the hard part for attackers. Time was. Reconnaissance takes time. Finding a usable credential takes time. Understanding an unfamiliar environment takes time.

AI agents change that math. Instead of one operator working through each step by hand, several agents can work through different parts of an environment at once, one testing credentials while another maps cloud resources while a third pokes at a CI/CD pipeline. The attacker stops moving through the kill chain step by step and starts running several branches of it simultaneously.

Picture the difference. A traditional intrusion looks roughly like: compromise, investigate, find credentials, move, repeat. An agentic one looks more like a fan-out: compromise, then in parallel, map the environment, search repositories, test credentials, identify cloud resources, poke at CI/CD, hunt for privileged access — all running at once instead of in sequence.

That parallelism is the real shift, and it's specifically bad news for anything resembling modern software delivery, where a repo, a CI runner, an API token, and a cloud deployment target are all sitting a few hops apart from each other.

The Part of This Story Developers Should Actually Care About

Most write-ups on this kind of intrusion focus on the SOC's problem. But look again at what the agents actually did: searched source repositories, created activity around CI/CD, obtained credentials, and moved into cloud infrastructure. That's not a SOC-only story. That's a software supply chain story.

A few uncomfortable questions worth sitting with if you maintain a pipeline:

How long does a leaked CI/CD token stay valid? If a token doesn't expire quickly and isn't scoped tightly, an agent that finds it has plenty of runway to use it.

Does your CI system log and alert on unexpected pipeline triggers, or would an off-schedule build just blend into normal noise? An automated attacker doesn't need to trigger a deploy at 3 AM to look suspicious if your pipeline runs constantly anyway.

Are your repo access tokens scoped to what they actually need, or does "make it work" access accumulate the same way over-permissioned service accounts do everywhere else? An agent doesn't need a superuser credential if a moderately-permissioned one gets it 80% of the way there.

Would unusual repository access even stand out? A read on a repo nobody usually touches is exactly the kind of low-noise signal that traditional monitoring wasn't built to catch, because it doesn't look like "an attack." It looks like someone browsing code.

None of these are new problems. They're the same access-hygiene questions security teams have been asking about human credentials for years. What's changed is the speed at which something can find and use a weak answer to any one of them.

Why the SOC's Bottleneck Is Now a Race Against Compute

Most SOCs already run into a familiar wall: too many alerts, too many dashboards, too few analysts. Add an automated attacker to that picture and a new asymmetry shows up. A human analyst might spend 20 minutes deciding whether an authentication anomaly matters. In that same 20 minutes, an automated attacker can be testing several other paths in parallel. The defender's bottleneck is human attention. The attacker's bottleneck is increasingly just compute, and compute scales a lot faster than headcount.

That's the actual problem underneath the AI-vs-AI framing people reach for. It's not really "attackers have AI so defenders need AI." It's that a tool watching only endpoint telemetry can't understand an attack moving through identity, cloud, source code, CI/CD, and AI infrastructure in the same hour. Neither can a tool watching only network traffic, or only identity. Each one has a piece of the story. The attacker doesn't care which product owns which alert. It cares about the relationships between systems, so that's what the defense has to see too.

Six Events, Six Alerts, or One Attack

Here's a version of how that plays out. A developer account authenticates from an unusual location. A few minutes later, that same identity accesses a repository it doesn't normally touch. Shortly after, a new API token gets created. Then a CI/CD pipeline kicks off unexpectedly. Not long after that, a cloud workload starts talking to a new external destination. And finally, a privileged account performs an administrative action nobody scheduled.

Looked at individually, that's six alerts, each with a plausible innocent explanation. Looked at together, in that order, in that timeframe, it's one attack. That gap between six isolated alerts and one understood attack is exactly where most SOCs are still losing time.

What an Attack Graph Gives You That an Alert List Doesn't

An alert list tells you something happened, somewhere, at some point. An attack graph asks a different set of questions: who initiated it, which identity was involved, which device was used, what changed afterward, which systems were touched, what credentials appeared, what network connections followed, which cloud resources got accessed, what happened next.

That second model is a lot closer to how these intrusions actually unfold, because attacks are relationships between events, not a pile of isolated ones. This is also why SIEM isn't going away even as the conversation shifts toward AI-driven detection. When an attack moves this fast, historical and cross-domain context is worth more, not less. The problem was never that SIEM collects too little data. It's that collecting data and understanding relationships between events are two different jobs, and most SOC workflows still treat the first one as if it were the second.

Detection Alone Buys You Very Little Time

Here's the part worth sitting with: detecting an event in seconds doesn't help much if it then takes two hours to figure out that the event is connected to five other things. A fast detection followed by a slow investigation is still, functionally, a slow response, and an automated attacker doesn't need much of that gap to keep moving.

That reframes what a security platform actually needs to be good at. Detection is table stakes. The harder, more valuable capability is compressing the distance between an event happening and someone (or something) understanding what it's actually connected to.

This Is the Problem Seceon Is Built Around

I'll be upfront: I work with Seceon, so weigh this accordingly.

What drew me to their approach specifically is that it doesn't try to solve this with a smarter alert. It's built so that an identity anomaly, an endpoint event, a network connection, and a cloud action can be evaluated together instead of separately, because that's the only way a six-alert sequence like the one above gets recognized as one attack instead of six tickets in six different queues. The AI part isn't the interesting bit on its own. What it has access to see, and whether it can connect that across identity, endpoint, network, and cloud, is what actually determines whether it's useful during something moving this fast.

The Analyst's Job Is Changing, Not Disappearing

None of this makes the human analyst less relevant. If anything, it raises the value of good judgment, because the routine work, opening repetitive alerts, manually cross-referencing dashboards, reconstructing an obvious attack chain by hand, is exactly the kind of thing that should get automated away. What shouldn't get automated away is deciding whether a high-confidence containment action is actually the right call, threat hunting for the stuff automation won't catch, and understanding what an incident actually means for the business behind it.

The goal was never to remove the analyst from the loop. It's to remove the waiting around the analyst.

The Real Takeaway

The significance of the Unit 42 investigation isn't just that AI was involved in an attack. It's that AI compressed a workflow that would normally require serious human effort and time into something that fit inside a single work morning. More than 50 techniques. Multiple layers of an enterprise. Credentials, repositories, CI/CD, cloud infrastructure. All inside a dramatically shorter window than defenders have historically had to work with.

That's not really a story about AI being scary. It's a story about the amount of time available for detection and response getting smaller, in systems a lot of engineering teams touch every single day. The attacker doesn't need to be smarter than the SOC. It just needs to be faster, and right now, speed is the thing most security architectures aren't built for.

FAQ

What is an agentic AI attack?
An agentic AI attack is an intrusion where a threat actor uses autonomous or semi-autonomous AI agents to carry out parts of the attack, reconnaissance, credential testing, lateral movement, without manually executing each step. This lets a single human operator run an intrusion at a speed and scale that would normally require a coordinated team.

How did attackers use AI in the Unit 42 investigation?
According to Unit 42's reporting, a human threat actor used frontier AI models and attack-specific agentic frameworks to automate large parts of an enterprise intrusion, compressing over 50 MITRE ATT&CK techniques into under 10 hours, including mapping the environment, searching source repositories, obtaining credentials, and triggering unauthorized CI/CD activity.

Why is CI/CD a target in AI-driven attacks?
CI/CD pipelines typically hold or generate credentials, deployment keys, and access to source code and cloud infrastructure, all in one place. For an automated agent searching for a fast path to privileged access, a pipeline with loosely scoped tokens or under-monitored triggers is a high-value target.

Can traditional SIEM tools detect agentic AI attacks?
SIEM remains useful for centralized, cross-domain visibility, but detecting an agentic attack usually requires correlating identity, endpoint, network, and cloud signals together rather than reviewing them in separate consoles. A SIEM that only stores logs without helping connect those signals across domains will still miss the pattern.

Does AI-driven attack automation mean human analysts become less important?
No. It shifts what analysts spend time on. Routine work like triaging repetitive alerts or manually reconstructing an attack chain becomes a better fit for automation, while human judgment becomes more valuable for validating high-confidence response actions, threat hunting, and understanding business impact.

Your Network Router May Be a Blind Spot. Attackers Are Counting on It.

Anurag Singh — Thu, 03 Sep 2026 09:30:32 +0000

A security team can monitor endpoints.

It can monitor identities.

It can collect cloud logs.

It can deploy EDR, XDR, SIEM and network monitoring.

But what happens when the infrastructure carrying the traffic is compromised?

That question became much more interesting after new research into the China-nexus threat actor known as Fire Ant.

Sygnia reported that Fire Ant expanded its activity beyond VMware environments and targeted Cisco IOS XR routers, TACACS authentication infrastructure and Linux management hosts.

The concerning part wasn't simply that routers were compromised.

It was what attackers did after getting control of trusted infrastructure.

The Router Wasn't Just a Router Anymore

A compromised endpoint usually gives an attacker access to a system.

A compromised router can give something much more valuable:

Perspective.

According to Sygnia's investigation, Fire Ant used compromised routers to capture network traffic and create covert connectivity.

That changes the role of the compromised device.

It is no longer just another asset inside the network.

It becomes a place from which the attacker can observe how the environment communicates.

And that's a very different security problem.

The Trust Layer Is Becoming an Attack Surface

Most security programs focus heavily on:

Endpoints
Servers
Applications
Cloud workloads
User identities

Network infrastructure can receive less attention because it is often treated as foundational infrastructure rather than an active security boundary.

But routers, authentication servers and management hosts sit in extremely privileged positions.

They help determine:

Where traffic goes
Who can authenticate
Which systems can communicate
How administrators manage infrastructure
What security telemetry gets generated

Compromise those layers and an attacker may gain more than access.

They may gain control over the environment's visibility and trust relationships.

When Your Logs Can't Be Trusted

This is one of the most interesting parts of the Fire Ant investigation.

Sygnia reported that the attackers manipulated telemetry and evidence sources, including router logging and authentication-related records.

That creates a difficult question for defenders:

What if the system generating your evidence has also been compromised?

Security teams often assume that logs are telling them what happened.

But sophisticated attackers can attempt to modify, suppress or bypass the evidence.

That means security monitoring cannot always depend on a single telemetry source.

One Alert May Not Tell the Story

Imagine this:

A network device shows an unusual configuration change.

At roughly the same time, an administrator account authenticates in an unusual way.

A management server starts communicating with an unexpected destination.

Network traffic begins moving through a previously unseen path.

Individually, each event may look explainable.

Together, they may describe an intrusion.

This is where cross-domain correlation becomes important.

The SOC needs to connect:

Network + Identity + Authentication + Endpoint + Configuration + Behavior

instead of investigating each alert independently.

Why Network Detection Needs More Context

Network monitoring can tell you that something unusual is happening.

But context tells you whether it matters.

For example:

Unusual network connection

is one signal.

But:

Unusual network connection + privileged identity + unexpected configuration change + abnormal management activity

is a much stronger investigation lead.

The difference isn't necessarily another detection rule.

It's the ability to connect the evidence.

The New Challenge for Security Teams

For years, security teams have asked:

“Are we collecting enough logs?”

The better question today may be:

“Are we collecting enough independent evidence to know when one source is lying?”

That is a very different security mindset.

A resilient SOC shouldn't depend entirely on one platform, one log source or one security control.

It should be able to compare signals across different parts of the environment.

Where SIEM Still Matters

This doesn't make SIEM less important.

It makes centralized security analytics more important.

A SIEM can provide the historical context needed to understand:

Authentication activity
Network events
Configuration changes
System activity
Privileged actions
Security alerts

But the real value comes from what the SOC can do with that data.

If every event remains isolated, analysts still have to manually connect the dots.

If events are correlated across multiple security domains, an investigation can start from a much stronger position.

Where XDR and NDR Add Context

NDR can help identify unusual network behavior.

XDR can connect signals across security layers.

UEBA can help identify abnormal user and entity behavior.

SIEM provides centralized investigation and historical context.

SOAR can help automate defined response actions.

The important part is not simply having all these technologies.

It's whether they can work together when an attacker moves between them.

Where Seceon OTM Fits

This is one reason a unified security architecture can be useful.

Seceon OTM brings together capabilities including SIEM, XDR, NDR, UEBA, SOAR, identity, cloud and other security telemetry within a unified security model.

For a network-focused incident, that means the investigation doesn't have to stop at:

“The router generated an alert.”

The SOC can look for related activity across:

Network
Identity
Endpoint
Cloud
Authentication
User behavior

That broader context can help security teams determine whether a network anomaly is simply an operational issue or part of a larger attack path.

The Blind Spot Isn't Always Where You Think

Security teams naturally focus on the systems that attackers are known to target.

But sophisticated threat actors don't always attack the final destination first.

Sometimes they attack the infrastructure that provides:

access

visibility

trust

connectivity.

That's why network infrastructure deserves the same security attention as endpoints and cloud workloads.

What Security Teams Should Reconsider

A modern security architecture should ask:

Are network devices continuously monitored?
Are authentication systems treated as high-value assets?
Can security teams detect unusual configuration changes?
Are network events correlated with identity activity?
Can telemetry from different sources be investigated together?
What happens if one logging source is compromised?
Are privileged infrastructure systems included in threat hunting?
Can the SOC validate suspicious activity using independent evidence?

These questions can expose gaps that a traditional “more alerts = more visibility” approach may miss.

The Bigger Lesson

Fire Ant is a useful reminder that attackers don't always need to break through the front door.

Sometimes they target the systems that control the doors.

Sometimes they target the systems that authenticate the people using those doors.

And sometimes they target the systems that tell defenders what happened.

That means cybersecurity visibility has to go beyond endpoints and applications.

The infrastructure that connects, authenticates and observes the environment needs to be part of the security picture too.

Because if attackers control the infrastructure you trust, the biggest problem may not be that you can't detect the attack.

It may be that you're looking at an incomplete version of what actually happened.

FAQ

Why are attackers targeting network infrastructure?

Network devices can provide privileged access, visibility into traffic and potential paths toward connected systems. Recent Fire Ant research showed how compromised Cisco routers were used as operational platforms for traffic collection and covert connectivity.

How can SIEM help detect compromised network infrastructure?

SIEM can centralize and correlate network, authentication, configuration and other security events. This can help analysts identify relationships that may not be visible when each telemetry source is investigated separately.

Can Seceon OTM monitor network and identity activity together?

Seceon OTM is designed to bring together security telemetry across areas including network, identity, endpoint, cloud and other security domains. This allows related activity to be investigated within a broader security context.

What is the role of NDR in detecting router-related attacks?

NDR can help identify unusual communication patterns, unexpected connections and abnormal network behavior. Its value increases when network signals can be correlated with identity, endpoint and authentication activity.

What should organizations do if they suspect a network device is compromised?

Organizations should preserve relevant evidence, validate configurations, review authentication activity, examine network telemetry and compare information across independent sources. A compromised device should not automatically be treated as a fully trustworthy source of forensic evidence.

Is monitoring endpoints enough for modern threat detection?

No.

Endpoints remain important, but modern attacks can involve network infrastructure, identity systems, cloud services, authentication platforms and management systems.

Security teams need visibility across the environment rather than focusing on one security layer.

Final Thought

The most dangerous blind spot in a network isn't always an unknown vulnerability.

Sometimes it's a trusted system nobody expected to become part of the attack.

Routers authenticate.

Routers connect.

Routers observe.

That makes them security assets, not just networking assets.

If the SOC doesn't have visibility into the infrastructure that connects the environment, it may be missing part of the attack story.

AI Is Coming for Critical Infrastructure. Can Traditional SOCs Keep Up?

Anurag Singh — Wed, 02 Sep 2026 07:05:56 +0000

A power grid does not get a second chance.

A water utility cannot simply “reset” its environment.

A manufacturing plant cannot treat an OT incident like another endpoint alert.

That is what makes the current shift in cyberattacks so important.

Attackers are increasingly using AI to accelerate reconnaissance, identify weaknesses and automate parts of the attack lifecycle.

Recent reporting has highlighted AI-enhanced attacks against interconnected energy systems, including operational technology environments.

The question is no longer whether AI will be used in cyberattacks.

It is:

Can security operations detect and respond at the same speed?

The Critical Infrastructure Attack Surface Has Changed

Critical infrastructure used to look relatively isolated.

Today, that picture is very different.

Power generation, utilities, manufacturing, transportation and other industrial environments increasingly connect:

IT networks
OT networks
Cloud services
Remote access systems
Identity platforms
IoT devices
Third-party systems
Industrial control systems

That connectivity creates operational advantages.

It also creates more paths into environments where a cyber incident can eventually become a physical or business disruption.

And attackers understand this.

AI Is Reducing the Time Between Discovery and Exploitation

AI doesn't necessarily need to invent an entirely new attack technique.

It can make existing techniques faster.

AI can help attackers:

Analyze large amounts of technical information
Identify potentially vulnerable systems
Automate reconnaissance
Generate or modify attack code
Improve social engineering
Find weaknesses across connected environments
Adapt attacks based on discovered information

That changes the defender's problem.

The issue isn't simply:

“Can we detect the attack?”

It's:

“Can we understand what is happening before the attacker moves to the next system?”

OT Security Makes This Even Harder

Traditional IT security often assumes that an affected machine can be isolated.

OT environments have different constraints.

A security team may be dealing with:

Legacy systems
PLCs
Industrial protocols
Long equipment lifecycles
Limited patch windows
Remote operational access
Safety requirements
Systems that cannot simply be taken offline

This creates a major SOC challenge.

An alert on an endpoint might look harmless by itself.

But combine it with:

identity activity + network behavior + unusual OT communication + endpoint telemetry

and the situation can look very different.

That's where correlation becomes more important than simply collecting more alerts.

The Problem Isn't a Lack of Security Data

Most modern organizations already generate enormous amounts of telemetry.

The problem is that the data often lives in different places.

One system sees the endpoint.

Another sees network traffic.

Another sees identity.

Another monitors cloud activity.

Another protects OT.

Another generates vulnerability alerts.

The SOC analyst is then expected to connect the dots.

That model becomes increasingly difficult when attackers are moving faster.

Detection without context creates noise.

Context without automation creates delay.

Automation without correlation creates risk.

A modern security operation needs all three:

Context + Correlation + Controlled Automation

What an AI-Driven SOC Should Actually Do

An AI SOC should not simply generate more AI-generated alerts.

That would make the problem worse.

Instead, AI should help security teams move through the investigation lifecycle faster.

A modern architecture should be able to:

Collect telemetry across IT, OT, identity, cloud, endpoint and network environments.
Normalize the data so different security signals can be understood together.
Correlate seemingly unrelated events into meaningful attack patterns.
Establish behavioral baselines to identify activity that doesn't fit normal behavior.
Investigate automatically before sending every event to an analyst.
Prioritize incidents based on risk and context rather than alert volume.
Respond within defined guardrails when automated action is appropriate.
Keep humans in control for decisions that require judgment, safety or business context.

This is a fundamentally different operating model from simply adding another detection tool.

Why SIEM Alone Isn't Enough

SIEM remains important because security teams need centralized visibility and historical context.

But modern environments require more than log collection.

Consider a hypothetical incident:

An employee account suddenly authenticates from an unusual location.

A few minutes later, an endpoint starts communicating with an unfamiliar internal host.

Network telemetry shows unusual traffic toward an OT segment.

An industrial device then begins communicating in a pattern that hasn't appeared before.

Looking at those events individually could produce several unrelated alerts.

Looking at them together could reveal a developing intrusion.

That is the difference between:

alert management

and

threat detection.

Where XDR, UEBA and SOAR Fit

This is where the modern SOC architecture becomes important.

SIEM provides centralized security data and investigation context.

XDR helps connect security signals across multiple control points.

UEBA helps identify unusual behavior involving users, entities and systems.

NDR provides visibility into network behavior.

OT security brings industrial environments into the security picture.

SOAR enables controlled response automation.

The real value comes when these capabilities aren't operating as completely disconnected islands.

Where Seceon OTM Fits

This is also where Seceon OTM fits into the broader AI SOC discussion.

Seceon OTM is designed around a unified security architecture that brings together capabilities including SIEM, XDR, SOAR, NDR, UEBA, ITDR, cloud and OT security.

The important idea isn't simply having all of those product names.

It is having the telemetry and security context available together so the SOC can investigate relationships between events.

For a critical infrastructure environment, that matters because an identity event, endpoint event, network event and OT event may all be pieces of the same attack.

Instead of asking:

“Which tool generated this alert?”

the SOC can ask:

“What is actually happening across the environment?”

That is a much more useful question.

The New SOC Metric: Time to Understand

Security teams have traditionally focused on metrics such as:

MTTD
MTTR
Alert volume
False-positive rate

Those metrics still matter.

But AI-driven attacks introduce another important question:

How long does it take the SOC to understand the attack?

Because detecting five separate alerts in five minutes isn't necessarily good detection.

If analysts need another two hours to understand that those alerts belong to one attack chain, the organization is still operating slowly.

The future SOC needs to reduce the distance between:

Event → Context → Investigation → Decision → Response

What Security Teams Should Look for in an AI SOC

If you're evaluating an AI-driven SOC platform, don't just ask:

“Does it use AI?”

Ask:

Can it correlate identity, endpoint, network, cloud and OT activity?
Can it reduce repetitive L1 investigation?
Can it establish behavioral baselines?
Can it automatically investigate related events?
Can analysts understand why an incident was prioritized?
Can response actions operate within defined guardrails?
Can it support hybrid and on-prem environments?
Can it handle multi-tenant environments for MSSPs?
Can it reduce dependence on multiple disconnected security tools?

Those questions are much more useful than simply comparing AI features on a datasheet.

Critical Infrastructure Doesn't Need More Noise

The cybersecurity industry has spent years adding more sensors.

The next challenge is making those sensors work together.

As attackers use AI to accelerate reconnaissance and exploitation, defenders need to shorten the time between an abnormal event and a meaningful security decision.

For IT environments, that can mean faster incident response.

For OT and critical infrastructure, it can mean something much more important:

preventing a cyber event from becoming an operational event.

The future of critical infrastructure security won't be defined by who has the most alerts.

It will be defined by who can understand, prioritize and respond to the right signals fastest.

And that is where the AI SOC becomes much more than another security product.

FAQ

How is Seceon OTM helping organizations build an AI SOC?

Seceon OTM brings together capabilities such as SIEM, XDR, SOAR, NDR, UEBA, ITDR, cloud and OT security in a unified security architecture.

The goal is to give security teams broader context across their environment so related identity, endpoint, network, cloud and OT signals can be correlated during an investigation.

What is the difference between Seceon OTM and a traditional SIEM?

A traditional SIEM primarily focuses on collecting, storing and analyzing security data.

Seceon OTM extends that model by combining SIEM with capabilities such as XDR, SOAR, NDR and UEBA, allowing organizations to move from simply collecting alerts toward correlation, investigation and automated response.

Can Seceon OTM support OT and critical infrastructure security?

Yes. Seceon OTM includes OT security capabilities alongside IT, network, endpoint, identity and cloud visibility.

This is particularly useful when organizations need to understand relationships between activity across IT and OT environments.

How does AI help reduce SOC alert fatigue?

AI can help correlate related events, establish behavioral baselines, investigate routine alerts and prioritize incidents based on context.

The objective isn't to create more automated alerts.

It is to reduce the number of alerts that analysts have to investigate manually.

Does an AI SOC replace security analysts?

No.

The strongest AI SOC model keeps humans involved for decisions that require judgment, governance, safety considerations and business context.

AI should remove repetitive investigation work so analysts can spend more time on complex threats and strategic security decisions.

What should organizations look for in an AI SOC platform?

Organizations should look beyond the “AI-powered” label.

Important capabilities include:

Cross-domain telemetry correlation
Behavioral analytics
Automated investigation
Threat prioritization
Controlled response automation
IT and OT visibility
Identity and endpoint context
Hybrid and on-prem deployment options
Reduced dependence on disconnected security tools

Final Thought

AI is changing the economics and speed of cyberattacks.

Critical infrastructure cannot afford to respond at yesterday's speed.

The answer isn't necessarily another security tool.

It is a security operation that can connect the signals, understand the attack and act quickly without removing humans from the decision-making process.

The faster attackers can connect the dots, the faster defenders need to do the same.

AI Agents Have Identities Too: Why Non-Human Identity Security Is Becoming a SOC Problem

Anurag Singh — Tue, 01 Sep 2026 09:09:05 +0000

Your security team knows how many employees have access to your systems.

But do you know how many AI agents, service accounts, API keys, and automated workflows have access?

That's becoming a much harder question in 2026.

Organizations are rapidly adopting AI agents to automate everything from software development and customer support to data analysis and security operations.

These agents don't just generate text.

They authenticate.

They access applications.

They call APIs.

They retrieve data.

They execute workflows.

And increasingly, they make decisions and take actions without a human sitting in front of every transaction.

That creates a new cybersecurity challenge:

AI agents need identities too.

And those identities need to be secured.

The Identity Problem Is Getting Bigger

For years, identity security focused primarily on human users.

Employees had accounts.

Administrators had privileged accounts.

Contractors had temporary access.

Security teams built policies around those identities.

Now add:

AI agents
Service accounts
API keys
Machine identities
Bots
Automated workflows
Cloud workloads

The number of identities inside an organization can grow far beyond its number of employees.

And unlike humans, these identities can operate 24/7.

That's where the risk becomes interesting.

An employee might access an application a few times during the day.

An automated agent might make thousands of API calls while nobody is watching.

So the question becomes:

How do you know when an AI agent is behaving abnormally?

What Happens When an AI Agent Gets Too Much Access?

Imagine an organization deploys an AI agent to help developers.

The agent has access to:

GitHub → Cloud APIs → CI/CD → Internal Documentation → Databases

Everything works normally for months.

Then something changes.

The agent suddenly starts:

Accessing repositories it never used before
Calling unfamiliar APIs
Requesting elevated permissions
Downloading unusual amounts of data
Communicating with an external service
Triggering workflows outside its normal pattern

None of these events necessarily means the agent has been compromised.

But together, they should raise a question:

Is this normal behavior for this identity?

That's where traditional identity monitoring starts becoming insufficient.

The New Security Question: What Is the Identity Doing?

Authentication tells you who or what accessed a resource.

Authorization tells you what it is allowed to access.

But security operations also need to understand:

What is it actually doing?

That's where behavioral analytics becomes important.

Consider two scenarios.

Scenario 1

An AI agent accesses the same API it uses every day.

The request volume is normal.

The destination is expected.

The action matches its assigned workflow.

Probably normal.

Scenario 2

The same agent suddenly accesses a sensitive database, requests elevated privileges, and sends data to an unfamiliar external endpoint.

The credentials may still be valid.

The authentication may still be legitimate.

But the behavior is unusual.

That's a security signal.

Why Traditional SIEM Visibility Isn't Always Enough

A SIEM can collect identity events.

It can collect API logs.

It can collect endpoint telemetry.

It can collect cloud activity.

It can collect network events.

But collecting everything doesn't automatically mean you understand what's happening.

The real value comes from connecting those signals.

For example:

AI agent authentication

↓

Privilege change

↓

Unusual API activity

↓

Endpoint anomaly

↓

Suspicious network connection

↓

Sensitive data access

Individually, each event might look manageable.

Together, they could represent an attack chain.

This is why modern security operations increasingly need SIEM + XDR + UEBA + threat intelligence + automated response working together.

Where UEBA Fits

UEBA stands for User and Entity Behavior Analytics.

And the word "entity" matters.

Because the entity doesn't always have to be a human.

It could be:

A user
A service account
An API key
A device
A workload
An AI agent

UEBA can help establish behavioral patterns and identify activity that deviates from those patterns.

For an AI agent, that could mean understanding:

Which applications does it normally access?

Which APIs does it normally call?

How frequently does it operate?

Which resources does it normally touch?

What does its normal behavior look like?

Once you understand the baseline, abnormal behavior becomes easier to identify.

This Is Where Identity Security Meets XDR

Identity security shouldn't exist in a separate security universe.

Suppose an AI agent suddenly behaves abnormally.

The next question isn't just:

"Is the identity suspicious?"

It's:

"What else is happening around it?"

Maybe the endpoint associated with the workflow is communicating with a suspicious destination.

Maybe another identity was compromised.

Maybe a privilege escalation happened immediately before the unusual API calls.

Maybe threat intelligence identifies the destination as malicious.

Now identity activity becomes part of a much larger investigation.

This is where XDR can provide additional context by connecting signals across different security layers.

Where Seceon OTM Fits

This is one of the reasons Seceon's Open Threat Management (OTM) Platform is relevant to this changing security model.

OTM brings together capabilities including:

SIEM + XDR + SOAR + UEBA + Threat Intelligence + Threat Hunting

within a unified security operations platform.

Instead of treating identity, endpoint, network, cloud and application activity as completely separate investigations, OTM is designed to correlate those signals and provide broader context.

For example:

Identity anomaly

↓

UEBA detects abnormal behavior

↓

XDR correlates endpoint and network activity

↓

Threat intelligence adds context

↓

SIEM provides the event history

↓

SOAR can automate an appropriate response

The goal isn't simply to detect that an AI agent did something unusual.

The goal is to understand:

What happened → why it matters → what else is connected → what should happen next

That's a much more useful security question.

AI Agents Are Also Creating a New Access-Control Problem

There's another issue that security teams shouldn't ignore.

AI agents need permissions.

And permissions can accumulate.

An agent might start with access to one application.

Then someone adds another integration.

Then another API.

Then another workflow.

Six months later, nobody remembers exactly why the agent has access to everything it can reach.

This is the same problem security teams have dealt with for years with human identities:

Excessive privileges.

The difference is that automated identities can operate much faster.

If an overprivileged human account is compromised, the attacker may have access to sensitive resources.

If an overprivileged AI agent is compromised or manipulated, it may be capable of taking automated actions across multiple systems.

That's why AI agent identity governance is becoming an important part of cybersecurity.

Security Teams Need an Inventory of Non-Human Identities

You can't protect what you don't know exists.

A practical starting point is building visibility into:

Who has access?

What applications are they connected to?

What credentials do they use?

What permissions do they have?

When were those permissions last reviewed?

What does normal behavior look like?

And for AI agents:

What actions are they actually capable of taking?

This isn't just an IAM problem anymore.

It's becoming part of security operations.

What Should Security Teams Monitor?

If your organization is deploying AI agents, consider monitoring:

1. Agent Identity

Know which AI agents exist and which credentials they use.

2. Permissions

Understand what resources each agent can access.

3. API Activity

Monitor unusual API calls, destinations, and request patterns.

4. Behavioral Changes

Identify activity that differs significantly from the established baseline.

5. Privilege Escalation

Watch for unexpected changes in permissions.

6. Data Access

Monitor unusual access to sensitive information.

7. Network Activity

Correlate agent behavior with network connections and endpoint activity.

8. Response Actions

Have clear policies for what should happen when an agent behaves abnormally.

The MSSP Challenge Is Even Bigger

For an MSSP, this problem scales quickly.

One customer may have a handful of AI agents.

Another may have hundreds of automated identities.

Another may be running thousands of service accounts and API integrations.

Now imagine monitoring all of that across dozens of customers.

The MSSP needs to understand:

Which identity belongs to which customer?

Is this behavior normal for that customer?

Is the same attack pattern appearing across multiple environments?

Which incident should be investigated first?

This is where centralized, multi-tenant security operations become increasingly important.

A platform such as Seceon OTM can help MSSPs bring identity, endpoint, network, cloud and application security signals into a unified operational workflow.

The objective isn't simply to monitor more identities.

It's to make those identities understandable at security-operations scale.

The AI Agent Security Checklist

Before deploying an AI agent into a production environment, security teams should be able to answer:

What identity does the agent use?
What permissions does it have?
Which applications can it access?
Which APIs can it call?
What data can it retrieve?
Can it create or modify resources?
Can it escalate its privileges?
How is its behavior monitored?
What happens if its behavior becomes abnormal?
How quickly can its credentials or access be revoked?

If those questions don't have clear answers, the agent may already represent an unmanaged attack surface.

The Bigger Shift in Identity Security

The traditional identity model was built around:

People → Accounts → Applications

The modern environment looks more like:

People + AI Agents + Service Accounts + APIs + Workloads → Applications + Data + Infrastructure

That's a much bigger identity ecosystem.

And security operations need visibility across all of it.

AI agents aren't going away.

Neither are automation, APIs, cloud workloads, or machine identities.

The organizations that adapt early will be the ones that treat these identities as first-class security entities, rather than invisible infrastructure.

FAQ: AI Agent Identity Security

What is non-human identity security?

Non-human identity security focuses on protecting machine identities such as service accounts, API keys, workloads, bots, and AI agents that authenticate and access organizational resources.

Why do AI agents need identity security?

AI agents often require credentials and permissions to access applications, APIs, data, and infrastructure. If those identities are compromised, overprivileged, or misused, the agent could potentially perform unauthorized actions.

What is AI agent identity governance?

AI agent identity governance involves managing an agent's identity, credentials, permissions, access lifecycle, and authorized actions throughout its operational lifetime.

Can UEBA monitor AI agents?

UEBA can analyze behavior from users and other entities. In environments where AI agents and automated identities generate telemetry, behavioral analytics can help identify activity that deviates from established patterns.

How can SIEM and XDR help with AI agent security?

SIEM can centralize identity, cloud, application, endpoint, and network events. XDR can help correlate related signals across security layers, giving analysts broader context when investigating suspicious activity.

How does Seceon OTM address this problem?

Seceon OTM combines SIEM, XDR, SOAR, UEBA, threat intelligence, and threat hunting in a unified security operations platform. This allows security teams to correlate identity behavior with endpoint, network, cloud, and other security telemetry.

Is AI agent security important for MSSPs?

Yes. MSSPs managing multiple customer environments need visibility into human and non-human identities across different organizations. Centralized and multi-tenant security operations can help make that monitoring more scalable.

The New Identity Perimeter

The identity perimeter is no longer just about employees.

It includes everything that can authenticate and take action.

Users.

Service accounts.

API keys.

Workloads.

AI agents.

And the security question is evolving with it.

It's no longer enough to ask:

"Who logged in?"

We need to ask:

"What identity is acting, what is it allowed to do, and does its behavior make sense?"

That's where identity security, UEBA, SIEM, XDR and automated response start coming together.

And as AI agents become more common, that combination may become less of an advanced capability and more of a baseline requirement.

AI agents are becoming part of the workforce.

Their identities need to become part of the security model too.

MFA Is Enabled. So Why Are Attackers Still Getting In?

Anurag Singh — Mon, 31 Aug 2026 08:52:29 +0000

Your organization has MFA. Your users have strong passwords. Conditional access is enabled. So how is an attacker still getting into the account?

This is one of the uncomfortable questions security teams are facing in 2026.

The problem is that attackers don't always need to defeat MFA.

Sometimes, they simply go around it.

Recent attacks against Microsoft 365 environments have included device-code phishing, session-token theft, password spraying and other techniques designed to obtain authenticated access even when MFA is enabled.

And that changes the identity security conversation.

The question is no longer:

"Do we have MFA?"

It is:

"What happens after authentication succeeds?"

MFA Protects the Login. What Protects the Session?

Think about what happens when you sign into a SaaS application.

You enter your credentials.

You complete MFA.

The application verifies you.

Then it gives your browser a session token.

From that point forward, the application often uses that session to recognize you.

That's convenient.

But it also creates another security problem.

If an attacker obtains that authenticated session, they may not need your password or another MFA challenge to continue operating as you.

The attacker isn't necessarily breaking MFA.

They're stealing the proof that MFA already happened.

This is why session-token theft and session hijacking have become important identity-security concerns.

The Attack Doesn't Always Look Like a Login Attack

Imagine this.

An employee signs into Microsoft 365 normally.

MFA succeeds.

Nothing looks suspicious.

A few minutes later, an attacker starts using a stolen session.

Now the security team sees:

An authenticated user accessing a SaaS application.

A new device or unusual location.

Unusual mailbox activity.

A privileged resource being accessed.

Large amounts of data being downloaded.

Suspicious communication from an endpoint associated with the account.

Individually, some of these events may not trigger a high-confidence incident.

Together, they tell a very different story.

That's where identity security becomes a security operations problem.

The Real Problem Is the Context Around the Identity

Identity systems can tell you that authentication happened.

Endpoint security can tell you what happened on the device.

Network security can tell you where the traffic went.

Cloud security can tell you what resources were accessed.

But an account takeover rarely stays inside one of those categories.

The attacker may move through all of them.

That's why modern detection needs to connect:

Identity + Endpoint + Network + Cloud + SaaS + User Behavior

Instead of asking:

"Was this login legitimate?"

Security teams increasingly need to ask:

"Does everything this identity is doing after the login make sense?"

This Is Where UEBA Becomes Important

User and Entity Behavior Analytics, or UEBA, isn't simply about detecting a suspicious login.

It's about understanding behavior.

For example:

A finance employee normally accesses a small group of applications during business hours.

Suddenly, the same identity accesses several unfamiliar resources, downloads large amounts of data, and starts interacting with systems it has never used before.

The login itself might have been valid.

The behavior isn't.

That's the kind of distinction behavioral analytics can help security teams identify.

And this is where identity signals become much more useful when correlated with the rest of the security environment.

Why SIEM Alone Isn't the Whole Answer

SIEM remains critical because security teams need centralized visibility and historical security data.

But collecting identity logs isn't the same as understanding an identity attack.

Consider this chain:

MFA authentication

↓

New device

↓

Unusual user behavior

↓

Suspicious endpoint activity

↓

Abnormal network connection

↓

Sensitive data access

↓

Potential account takeover

If every signal lives in a different security product, the analyst has to manually connect the dots.

That costs time.

And during an active attack, time matters.

Where Seceon OTM Fits

This is one of the areas where Seceon's Open Threat Management (OTM) Platform can be relevant.

OTM brings together capabilities including:

SIEM + XDR + UEBA + SOAR + Threat Intelligence + Threat Hunting

within a unified security operations platform.

That matters because an identity anomaly shouldn't necessarily be investigated as an isolated identity event.

It can be correlated with endpoint activity.

Network behavior.

Cloud activity.

Threat intelligence.

And other security signals.

For example:

An unusual login occurs.

UEBA identifies behavior outside the normal baseline.

XDR connects the identity activity with an endpoint anomaly.

Threat intelligence adds context around suspicious infrastructure.

SIEM provides the broader event history.

SOAR can help automate appropriate response actions.

Now the analyst isn't looking at six unrelated alerts.

They're looking at a potential attack chain.

The identity is the starting point. The surrounding behavior tells the story.

The Biggest Identity Security Mistake

One of the biggest mistakes organizations can make is treating authentication as the finish line.

Authentication should be the beginning of continuous trust evaluation.

A user successfully passing MFA doesn't automatically mean:

Every subsequent action is legitimate
Every device associated with the session is trustworthy
Every resource request is normal
Every application interaction is expected
Every session should remain valid

Security needs to continue after authentication.

That's especially important as organizations rely more heavily on cloud applications, SaaS platforms, APIs and remote access.

What Should Security Teams Monitor After MFA?

If you're reviewing your identity-security strategy in 2026, don't stop at MFA deployment.

Look at what happens after the user authenticates.

Session Activity

Monitor unusual sessions, devices, locations and access patterns.

User Behavior

Look for activity that significantly differs from the user's normal behavior.

Privilege Changes

Watch for unexpected privilege escalation or access to sensitive resources.

Application Activity

Monitor unusual SaaS and cloud application usage.

Endpoint Signals

Correlate identity activity with what is happening on the user's device.

Network Behavior

Look for unusual destinations, connections or traffic patterns associated with the identity.

Data Access

Watch for abnormal downloads, transfers or access to sensitive information.

The important word here is:

Correlation.

One event rarely tells the whole story.

What About Phishing-Resistant MFA?

This doesn't mean MFA is no longer useful.

Quite the opposite.

Strong authentication remains an important security control, and phishing-resistant methods can significantly improve resistance to credential-based attacks.

But identity security shouldn't depend on a single control.

Even with stronger authentication, organizations still need visibility into:

What happened after access was granted?

Because attackers don't always need to compromise the authentication mechanism itself.

They may compromise the endpoint.

Steal an active session.

Abuse an authorized identity.

Exploit excessive permissions.

Or operate through a trusted application.

Why This Matters for MSSPs

For an MSSP, identity monitoring becomes even more complicated.

Imagine managing security for 50 customers.

Each customer has:

Different identity providers.

Different SaaS applications.

Different users.

Different access policies.

Different normal behavior.

Different risk profiles.

An MSSP can't realistically investigate every identity event manually.

The platform needs to help separate:

Normal behavior → Suspicious behavior → Potential attack

and provide enough context for analysts to act.

This is where a unified security operations approach can become valuable.

With Seceon OTM, MSSPs can bring SIEM, XDR, UEBA, SOAR and threat intelligence into a centralized security operations workflow rather than treating identity activity as a completely separate security problem.

The Identity Perimeter Is Changing

The old security model was largely:

Protect the network perimeter.

Then it became:

Protect the endpoint.

Then:

Protect the identity.

But today, none of these exist in isolation.

An identity can be compromised through an endpoint.

An endpoint can be controlled through a malicious application.

A compromised identity can access cloud resources.

Cloud activity can lead to data exposure.

And network activity can reveal the attack.

The modern security perimeter is increasingly a connected ecosystem.

That's why security teams need visibility across the entire attack chain.

The Question Security Teams Should Be Asking

Don't ask only:

"Is MFA enabled?"

Ask:

"If an attacker gets past the login, how quickly would we know?"

That's a much harder question.

And it's the one that matters.

Because authentication tells you who successfully entered.

Security operations need to determine whether that person is actually behaving like themselves.

That's where identity analytics, UEBA, XDR, SIEM, threat intelligence and automated response start working together.

And that's the direction Seceon is taking with OTM.

Not just detecting the login.

Understanding what happens next.

FAQ: MFA Bypass and Identity Security

Can attackers bypass MFA?

Yes. Attackers can use techniques such as adversary-in-the-middle phishing, device-code phishing, MFA fatigue, credential theft and session-token theft to obtain or abuse authenticated access.

Does MFA prevent account takeover?

MFA significantly reduces the risk of many credential-based attacks, but it isn't a complete account-takeover defense. Attackers can target sessions, tokens, endpoints, applications and users after authentication.

What is session-token theft?

Session-token theft occurs when an attacker obtains a token representing an already authenticated session and uses it to impersonate the legitimate user.

What is UEBA?

UEBA stands for User and Entity Behavior Analytics. It analyzes behavioral patterns to identify activity that deviates from what is considered normal for a user or entity.

How can SIEM help detect identity attacks?

SIEM can centralize identity, endpoint, network, cloud and application events, making it easier to investigate activity across different security domains and build a timeline of an incident.

How does Seceon OTM help with identity-related threats?

Seceon OTM combines SIEM, XDR, UEBA, SOAR, threat intelligence and threat hunting to correlate security signals across different environments. This can help security teams connect unusual identity behavior with endpoint, network and other security events.

Is Seceon OTM useful for MSSPs?

Yes. OTM is designed to support centralized and multi-tenant security operations, helping MSSPs monitor and investigate security activity across multiple customer environments.

MFA Was Never Supposed to Be the Whole Security Strategy

MFA is important.

Strong authentication is important.

Identity controls are important.

But none of them answer the most important question by themselves:

What is this identity doing right now?

That's the question modern security operations need to answer continuously.

Because the attacker doesn't care whether your dashboard says:

"MFA: Enabled."

They care whether they can operate as a trusted identity after authentication.

And that's why identity security is no longer just an IAM problem.

It's a SOC problem.

AI Agents Are Becoming a Security Problem: What SOC Teams Need to Monitor in 2026

Anurag Singh — Thu, 27 Aug 2026 08:37:47 +0000

AI agents don't just answer questions anymore. They access systems, use tools, call APIs, retrieve data, and take actions.

That changes cybersecurity.

For years, security teams built their monitoring around a fairly familiar model:

Users → Devices → Applications → Networks

Now there's another layer:

AI Agents → Tools → APIs → Applications → Data

And the security challenge isn't simply protecting the AI model.

It's understanding everything the AI agent can access and everything it can do.

The New Security Problem: AI That Can Act

An AI chatbot that answers a question is one thing.

An AI agent that can:

Read company documents
Query databases
Access SaaS applications
Call APIs
Execute code
Modify files
Trigger workflows
Communicate with other systems

is something completely different.

It becomes an active participant in the environment.

That means security teams need to start asking questions that sound much more like traditional identity and access management questions:

Who is this agent?

What is it allowed to access?

Which tools can it use?

What data can it retrieve?

What does normal behavior look like?

What happens if the agent is manipulated?

This is why AI agent security is quickly becoming a serious cybersecurity topic in 2026.

AI Agents Create a New Attack Surface

The interesting part is that attackers don't necessarily need to compromise the AI model itself.

They can target the environment around it.

Imagine this sequence:

An AI agent authenticates successfully.

↓

It accesses an internal application.

↓

It retrieves data it doesn't normally request.

↓

It calls an unfamiliar API.

↓

A related endpoint starts communicating with a suspicious destination.

↓

Another identity suddenly performs an unusual action.

Individually, these events may not look catastrophic.

Together, they could tell a completely different story.

The attack is one story. The telemetry is fragmented.

That's the problem modern SOC teams need to solve.

Why SIEM Alone Isn't Enough

SIEM remains an important part of the security stack because it provides centralized visibility into security events.

But modern environments are generating signals from everywhere:

Identity + Endpoint + Network + Cloud + SaaS + API + Application + AI

Collecting those events is only the beginning.

Security teams also need to:

Correlate → Investigate → Prioritize → Respond

That's where the convergence of SIEM, XDR, UEBA and SOAR becomes important.

SIEM helps collect the evidence.

XDR helps connect signals across security layers.

UEBA helps identify abnormal behavior.

SOAR helps automate response.

Threat intelligence adds external context.

AI/ML can help process and correlate large volumes of security telemetry faster.

The real challenge is making those capabilities work together.

Where Seceon OTM Fits

This is the problem Seceon is addressing with its Open Threat Management (OTM) Platform.

OTM brings together SIEM, XDR, SOAR, UEBA, threat hunting and threat intelligence in a unified security operations platform.

Instead of forcing analysts to investigate every security domain separately, OTM is designed to ingest and correlate telemetry across:

Networks → Endpoints → Cloud → Applications → Identities

That becomes increasingly important as AI agents become another source of activity inside enterprise environments.

Consider the example above again.

An unusual identity event.

An abnormal SaaS action.

Suspicious API activity.

An endpoint anomaly.

Network communication with a known malicious destination.

A fragmented security stack can leave analysts jumping between multiple consoles.

A unified security operations platform can provide the context needed to understand whether those events are connected.

That's the difference between seeing alerts and understanding an attack.

AI Security and AI-Powered Security Operations Are Different

This distinction is becoming important.

AI Security

AI security focuses on protecting:

AI models
AI applications
AI agents
Agent permissions
AI data
APIs and tools
AI workflows
Model interactions

AI-Powered Security Operations

AI-powered security operations focuses on improving:

Threat detection
Event correlation
Behavioral analysis
Investigation
Threat hunting
Alert prioritization
Automated response

Organizations deploying AI agents increasingly need both.

You need to secure the AI.

You also need intelligent security operations around the AI.

What Should SOC Teams Monitor?

If your organization is deploying AI agents, don't only monitor whether the model is functioning correctly.

Monitor the security context around it.

1. Agent Identity

Know which agents exist and which identities or credentials they use.

2. Permissions

Understand what each agent is allowed to access.

3. Tool Usage

Monitor which tools, APIs, applications and services agents are calling.

4. Behavioral Changes

An agent performing a normal task today doesn't mean the same behavior is normal tomorrow.

Behavioral analytics can help identify unusual activity.

5. Data Access

Track what information agents retrieve and whether that access matches their expected role.

6. Network Activity

AI agents still operate within networks.

Unexpected external communication can become an important security signal.

7. Cross-Domain Activity

The most important clue may not exist in one log.

It may appear only when identity, endpoint, network, cloud and application events are correlated.

Why This Matters Even More for MSSPs

For an internal SOC, monitoring one organization's AI agents is already a challenge.

For an MSSP, the problem scales differently.

One customer might have ten agents.

Another might have fifty.

Another might have hundreds of automated workflows.

Now the MSSP needs to answer:

Which customer does this agent belong to?

Is this behavior normal for that customer?

Is the same attack pattern appearing across multiple environments?

Which incident should the analyst investigate first?

This is where fragmented security operations become difficult to scale.

A unified, multi-tenant platform such as Seceon OTM can help MSSPs bring security monitoring, correlation, behavioral analytics and response capabilities into a more centralized operating model.

The goal isn't simply adding another AI security product.

The goal is making security operations scalable enough for an environment where machines are increasingly acting on behalf of people.

The Biggest Mistake: Treating AI Agents Like Normal Applications

An application generally does what it was programmed to do.

An AI agent can determine what action to take based on context.

That difference matters.

An agent may interact with multiple systems in a single workflow.

It may retrieve information from one application and use that information to make a decision somewhere else.

It may call tools dynamically.

It may encounter untrusted content.

And it may operate at machine speed.

That means traditional security controls still matter, but visibility and behavioral context become increasingly important.

What a Modern AI SOC Should Look Like

The future SOC isn't simply:

More alerts + more dashboards + more analysts

It needs to become:

More context + better correlation + faster investigation + smarter automation

That's why the discussion around AI SOC, SIEM, XDR, SOAR and UEBA is becoming increasingly connected.

AI agents are changing the attack surface.

AI can also change how defenders respond to that attack surface.

The organizations that benefit most will be the ones that can do both without creating another collection of disconnected security tools.

FAQ: AI Agent Security and AI SOC

What is AI agent security?

AI agent security focuses on protecting autonomous AI systems that can access data, tools, APIs, applications and other systems. It includes identity, permissions, monitoring, behavioral analysis and protection against malicious or unintended actions.

Why are AI agents a cybersecurity risk?

AI agents can operate with permissions and take actions across multiple systems. If an agent is manipulated or compromised, its access could potentially be abused to retrieve data, call tools or trigger workflows.

What is an AI SOC?

An AI SOC uses artificial intelligence and automation to improve security operations including detection, correlation, investigation, prioritization, threat hunting and response.

How do SIEM, XDR, UEBA and SOAR work together?

SIEM provides centralized security event visibility. XDR correlates signals across security layers. UEBA analyzes behavioral anomalies. SOAR automates investigation and response workflows. Together, they can provide a more complete security operations workflow.

How does Seceon OTM support AI-powered security operations?

Seceon OTM is an Open Threat Management platform combining SIEM, XDR, SOAR, UEBA, threat hunting and threat intelligence with AI/ML-driven security analytics. It is designed to correlate telemetry across networks, endpoints, cloud environments, applications and identities.

Is Seceon OTM an AI security platform?

Seceon OTM is primarily a unified security operations platform rather than a dedicated AI model-security product. Its focus is broader visibility, correlation, behavioral analytics, threat detection and automated response across the security environment.

Is Seceon OTM suitable for MSSPs?

Yes. OTM is designed to support centralized and multi-tenant security operations, making it relevant for MSSPs managing security across multiple customer environments.

The SOC Is Changing

AI agents are becoming more capable.

They're moving from answering questions to taking actions.

That means the security team needs to know:

Who is acting?

What are they accessing?

Why are they doing it?

Is that behavior normal?

And what happens next?

The answer won't come from another isolated dashboard.

It will come from connecting the signals.

Identity. Endpoint. Network. Cloud. Application. AI.

That's the direction security operations are moving toward.

And that's why platforms such as Seceon OTM are focusing on bringing SIEM, XDR, SOAR, UEBA, threat intelligence and AI-driven correlation together in one operational view.

**AI agents may be changing the attack surface.

The SOC needs to change with them.**

How is your security team approaching AI agents today — as applications, identities, or a completely new security category?

MCP Security in 2026: Why AI Agents Are Creating a New Attack Surface

Anurag Singh — Wed, 26 Aug 2026 07:05:56 +0000

AI agents are getting access to more tools, more data, and more systems. The security problem isn't the AI anymore. It's everything the AI can reach.

For the last few years, cybersecurity teams have been focused on protecting users, endpoints, applications, cloud workloads, and APIs.

Now there's another entity entering the environment:

AI agents.

And unlike a traditional application, an AI agent can make decisions, call tools, access data, interact with APIs, and potentially trigger actions without a human approving every individual step.

That changes the security model.

One of the technologies accelerating this shift is the Model Context Protocol (MCP).

MCP makes it easier for AI applications and agents to connect with external tools, services, and data sources.

That's powerful.

It's also a security problem.

What Is MCP and Why Does Security Matter?

Think of MCP as a bridge between an AI application and the tools it needs to accomplish a task.

An AI agent might need to:

Query a database
Read files
Access SaaS applications
Call an API
Search internal information
Execute a workflow
Interact with another service

MCP can make these connections easier to build.

But every new connection creates another trust boundary.

And every trust boundary creates another security question.

Who is the agent?

What is it allowed to access?

Which tools can it call?

What data can it retrieve?

What happens if the agent is manipulated?

Can security teams see what it actually did?

These questions are becoming increasingly important as organizations move from AI assistants toward autonomous and agentic AI systems.

The AI Agent Attack Surface Is Bigger Than the AI Model

A common mistake is to think about AI security as only a model-security problem.

The model is only one part of the environment.

A more realistic architecture looks like this:

User → AI Agent → MCP → Tools → APIs → Applications → Data

Now imagine an attacker influencing one part of that chain.

The attacker doesn't necessarily need to compromise the AI model itself.

They may instead try to manipulate the agent into:

Accessing information it shouldn't
Calling an unauthorized tool
Retrieving sensitive data
Following malicious instructions
Using excessive permissions
Triggering an unexpected workflow
Communicating with an untrusted service

That's why AI agent security is becoming closely connected to identity security, API security, cloud security, application security, and security operations.

MCP Security Is Also an Identity Problem

Traditional security was largely built around human identities.

An employee logs in.

A security team monitors the account.

Permissions are assigned.

Activity is logged.

An AI agent introduces a different type of identity.

It may have credentials.

It may have permissions.

It may have access to APIs.

It may be able to execute actions.

And it can operate much faster than a human.

That makes non-human identity security increasingly important.

The question security teams need to answer isn't simply:

"Is this AI agent secure?"

It's:

"Can we continuously understand what this agent is doing, what it has access to, and whether that behavior is normal?"

Where SIEM, XDR, UEBA and SOAR Enter the Picture

This is where the conversation moves beyond AI application security.

An organization may deploy a dedicated AI security control for protecting an agent or MCP environment.

But security operations still need visibility into what happens around it.

Consider this:

An AI agent authenticates.

Then it accesses an application.

Then it calls an API.

Then unusual data is retrieved.

Then an endpoint starts communicating with a suspicious destination.

Then another identity performs an abnormal action.

These aren't necessarily separate incidents.

They may be one attack chain.

This is where security operations platforms become important.

SIEM provides centralized security telemetry.

XDR helps correlate signals across security layers.

UEBA helps identify abnormal behavior.

SOAR helps automate response.

Threat intelligence adds external context.

And AI/ML can help security teams process and correlate large amounts of telemetry faster.

The challenge is making these capabilities work together.

This Is Where Seceon OTM Fits

Seceon's Open Threat Management (OTM) Platform is built around this unified security operations model.

OTM combines SIEM, XDR, SOAR, UEBA, threat hunting, and threat intelligence within a single AI/ML-driven security platform.

Instead of treating every security signal as an isolated event, OTM is designed to ingest telemetry from networks, endpoints, cloud services, applications, and identities, normalize that information, and correlate it to provide broader security context.

That becomes particularly relevant as organizations introduce AI agents into their environments.

For example:

An AI agent performs an unusual authentication.

The related application activity looks abnormal.

Network behavior changes.

An endpoint starts communicating with a suspicious destination.

UEBA detects behavior outside the normal baseline.

Instead of asking an analyst to manually investigate five different security consoles, a unified security operations approach can help connect those signals into a larger picture.

**The agent may be new.

The security operation around it doesn't have to be fragmented.**

AI Security vs. AI-Powered Security Operations

These two concepts are often mixed together.

They shouldn't be.

AI Security

AI security focuses on protecting:

AI models
AI applications
AI agents
Prompts and context
AI data
MCP connections
Tools and APIs
Agent permissions

AI-Powered Security Operations

AI-powered security operations focuses on using AI to improve:

Threat detection
Event correlation
Behavioral analysis
Investigation
Threat hunting
Alert prioritization
Automated response

A mature security strategy increasingly needs both.

**You need to secure the AI.

And you need to use AI to secure the environment around it.**

What Should Security Teams Look for in an AI Agent Security Platform?

If you're evaluating AI security platforms, AI agent security tools, MCP security solutions, or AI SOC platforms in 2026, don't simply ask whether the vendor has an "AI security" product.

Ask what the platform actually helps you see and control.

1. Agent visibility

Can you identify the AI agents operating in your environment?

2. Identity and permissions

Can you understand which identities, credentials, and permissions are associated with agents?

3. Tool and API activity

Can security teams monitor the tools and APIs being accessed?

4. Behavioral analytics

Can the platform detect unusual behavior rather than relying only on known signatures?

5. Cross-domain correlation

Can it connect identity, endpoint, network, cloud, application, and API signals?

6. Threat intelligence

Can suspicious activity be enriched with external threat intelligence?

7. Automated response

Can security teams automate appropriate response actions when a threat is confirmed?

8. MSSP and multi-tenant support

For MSSPs, can multiple customer environments be monitored without creating a completely separate security operation for every customer?

These questions matter because AI agents are not going to exist in isolation.

They will operate inside the same environments that security teams are already responsible for protecting.

Why MSSPs Should Pay Special Attention

For an MSSP, the problem gets even bigger.

One AI agent is manageable.

Hundreds of customers deploying hundreds of AI workflows is a different problem.

Now the MSSP has to understand:

Which customer has the agent?

What does that agent access?

Is its behavior normal?

Is the activity malicious?

Does the same behavior appear across multiple customers?

This is where fragmented security operations can become difficult to scale.

A unified platform such as Seceon OTM is designed around centralized, multi-tenant security operations, bringing capabilities such as SIEM, XDR, UEBA and SOAR into one environment.

For MSSPs, the objective isn't simply adding another AI security product.

It's being able to operate security at scale as customers adopt more autonomous technology.

The Future of MCP Security Isn't Just MCP Security

This may be the most important part.

MCP is only one component of a much larger shift.

AI agents are becoming connected to:

Identity → SaaS → APIs → Cloud → Applications → Data → Other Agents

Every connection increases the potential attack surface.

So organizations shouldn't build an AI security strategy around one protocol alone.

They need visibility across the entire environment.

That's why SIEM, XDR, UEBA, SOAR, threat intelligence, identity security, and AI security are increasingly becoming connected conversations.

The security operation needs to understand the entire chain.

FAQ: AI Agent Security, MCP Security and AI SOC

What is MCP security?

MCP security refers to protecting the connections between AI applications or agents and the tools, services, APIs, and data sources they can access through the Model Context Protocol. Key concerns include authorization, tool access, trust boundaries, monitoring, and misuse.

Why is AI agent security important in 2026?

AI agents can increasingly interact with systems and take actions autonomously. That means a compromised or manipulated agent could potentially access data, invoke tools, or trigger workflows beyond what security teams intended.

Is MCP the same as AI security?

No. MCP is one technology used to connect AI systems with external capabilities. AI security is broader and includes protecting models, agents, identities, data, APIs, tools, applications, and AI workflows.

What is an AI SOC platform?

An AI SOC platform uses AI and automation to improve security operations such as detection, correlation, investigation, prioritization, threat hunting, and response. A unified platform can combine capabilities such as SIEM, XDR, SOAR, and UEBA.

How does Seceon OTM fit into AI security?

Seceon OTM is a unified Open Threat Management platform combining SIEM, XDR, SOAR, UEBA, threat hunting, and threat intelligence with AI/ML-driven analytics. It is designed to correlate telemetry across networks, endpoints, cloud services, applications, and identities.

Is Seceon OTM an MCP security platform?

Seceon OTM should not be confused with a dedicated MCP runtime security control. Its value is broader security operations visibility and correlation across the environment surrounding applications, identities, endpoints, networks, cloud services, and other telemetry sources.

What is the best AI SOC platform in 2026?

There isn't one universal answer. The right choice depends on the organization's architecture and requirements. Teams evaluating an AI SOC platform should compare detection, correlation, UEBA, XDR, SOAR, threat intelligence, automation, integrations, and operational complexity.

Can MSSPs use Seceon OTM?

Seceon OTM is designed to support unified and multi-tenant security operations, making it relevant for MSSPs managing multiple customer environments and looking to consolidate security monitoring, detection, correlation, and response capabilities.

The Bigger Security Shift

For years, cybersecurity was built around protecting people and machines.

Now we're entering an environment where software can have:

Identity.

Permissions.

Memory.

Tools.

Access to data.

And the ability to act.

That's what makes agentic AI different.

The biggest AI security challenge may not be protecting the model itself.

It may be controlling everything the model is allowed to touch.

And that's why the future of AI security will require more than another isolated security product.

It will require visibility, identity context, behavioral analytics, correlation, threat intelligence, and automated response working together.

That's the problem unified security platforms such as Seceon OTM are built to address.

**AI agents are becoming more autonomous.

Security operations need to become more intelligent.**

Are AI agents becoming the next major non-human identity security problem, or are organizations overestimating the risk?

I'd be interested to hear how security teams are approaching MCP security, AI agent security, and AI SOC adoption in 2026.

Best AI Security Platform 2026: Securing Agentic AI, AI Agents, and the New Non-Human Identity

Anurag Singh — Tue, 25 Aug 2026 08:20:21 +0000

Your newest privileged identity may not be a person. It may be an AI agent.

AI agents are moving quickly from simple chat interfaces to systems that can access applications, call APIs, retrieve data, use tools, make decisions, and execute multi-step workflows.

That changes the cybersecurity problem.

An AI agent with access to sensitive systems isn't just an AI application anymore.

It's an identity with permissions.

And in 2026, that identity needs to be secured like one.

AI security is becoming a major cybersecurity conversation as organizations deploy increasingly autonomous systems, creating new challenges around identity, authorization, prompt injection, data access, and automated actions.

That raises a bigger question for security teams:

What is the best AI security platform for protecting AI agents while still securing everything those agents can access?

AI Agent Security Is Becoming a Cybersecurity Problem

Traditional security programs were designed around familiar entities:

Users → Devices → Applications → Networks

Agentic AI adds another layer:

AI Agents → Tools → APIs → Data → Other Agents

An agent might access Microsoft 365, query a database, call an API, retrieve internal documents, or trigger an automated workflow.

Every one of those connections creates security decisions around identity, authorization, behavior, and access.

So the problem isn't simply:

"How do we secure the AI model?"

It's:

"How do we secure what the AI agent can see, access, and do?"

What Makes an AI Security Platform Different?

There are already many products marketed around AI security.

But they don't all solve the same problem.

Some focus on securing AI models.

Some focus on AI applications.

Some focus on runtime authorization.

Some use AI to improve security operations.

And traditional cybersecurity platforms such as SIEM, XDR, SOAR, EDR, and UEBA continue to provide the visibility and response capabilities organizations already depend on.

That's why organizations evaluating the best AI cybersecurity platform in 2026 should first understand the difference between:

Securing AI

and

Using AI to improve security operations.

The strongest security architecture increasingly needs to address both.

AI Security vs. AI-Powered Security Operations

Security Need	What Organizations Need
AI Agent Security	Protect agents, permissions, tools, APIs, and workflows
Identity Security	Monitor human and non-human identities
SIEM	Collect, normalize, search, and correlate security telemetry
XDR	Connect detection signals across security layers
UEBA	Identify abnormal user and entity behavior
SOAR	Automate investigation and response workflows
AI SOC	Apply AI to detection, investigation, prioritization, and response

The important point is that these aren't necessarily competing technologies.

They can be layers of the same security operation.

And that's where unified security platforms become interesting.

Where Seceon Fits Into AI Security

Seceon's Open Threat Management (OTM) Platform takes the second approach: using AI/ML-driven security analytics across a broader security operations architecture.

OTM combines SIEM, XDR, SOAR, UEBA, threat hunting, and threat intelligence in a unified platform, ingesting telemetry from networks, endpoints, cloud services, applications, and identities and correlating it in real time.

That becomes particularly relevant as organizations introduce AI agents into their environments.

Consider a simple scenario.

An AI agent authenticates successfully.

It accesses a SaaS application.

It retrieves data it doesn't normally request.

It calls an unfamiliar API.

And shortly afterward, an endpoint begins communicating with a suspicious external destination.

A traditional security architecture may see those as separate events.

Identity event. SaaS event. API event. Endpoint event. Network event.

But the potential attack is one story.

That's where Seceon's OTM approach is relevant: correlate activity across security domains rather than forcing analysts to investigate every signal independently.

The more autonomous the environment becomes, the more important that context becomes.

What Should You Look for in the Best AI Security Platform?

If you're evaluating AI security platforms in 2026, don't stop at:

"Does this product use AI?"

That's no longer a meaningful differentiator by itself.

Instead, ask:

1. Can it see AI-related activity?

Can the security architecture monitor identities, applications, APIs, endpoints, cloud environments, and network activity associated with AI workflows?

2. Can it understand behavior?

AI agents don't always behave like traditional applications.

Behavioral analytics and UEBA can help identify activity that deviates from established patterns.

3. Can it correlate the attack?

Can the platform connect identity, endpoint, network, cloud, SaaS, and application signals?

4. Can it prioritize the threat?

Security teams don't need another flood of alerts.

They need to know which activity deserves attention first.

5. Can it respond?

Detection without response still leaves security teams with manual work.

SOAR and automated response can help close the gap between identifying suspicious activity and taking action.

6. Can it work across the existing security stack?

AI security shouldn't create another isolated security silo.

The platform needs to fit into the broader SIEM + XDR + SOAR + identity + cloud security ecosystem.

Why AI Agents Make SIEM, XDR, and SOAR More Important

It might seem that AI agents will replace traditional security platforms.

The opposite may be closer to reality.

As AI agents create more identities, more connections, and more autonomous actions, security teams need more context, not less.

SIEM provides the visibility.

XDR connects security signals.

UEBA helps identify behavioral anomalies.

SOAR automates response.

Threat intelligence adds external context.

And AI can help bring those capabilities together faster.

That's the direction behind Seceon's OTM Platform: a unified security architecture where SIEM, XDR, SOAR, UEBA, threat intelligence, and AI/ML-driven analytics operate as part of the same security workflow.

The objective isn't to replace every security tool with AI.

It's to make the security operation intelligent enough to keep up with an increasingly autonomous environment.

The AI Security Platform Landscape in 2026

Organizations evaluating the market will encounter different approaches.

Some vendors focus primarily on AI application and model security.

Others focus on AI-powered SOC automation.

Traditional cybersecurity leaders continue expanding SIEM, XDR, EDR, SOAR, and AI-assisted security operations.

And unified platforms such as Seceon OTM position themselves around bringing multiple security capabilities together rather than forcing organizations to build an increasingly complex collection of disconnected tools.

There is no single platform that is automatically the best for every organization.

The right question is:

Which platform addresses the security problems created by your environment without creating another layer of operational complexity?

For organizations looking for AI-powered security operations combined with SIEM, XDR, SOAR, UEBA, threat intelligence, and automated response, Seceon OTM is built specifically around that unified model.

AI Agent Security Is Also Identity Security

One of the biggest changes coming from agentic AI is the growth of non-human identities.

An employee can have an identity.

A service account can have an identity.

An application can have an identity.

And now an AI agent can have one too.

The difference is that an AI agent may be able to make decisions and take actions at machine speed.

That makes questions around identity, authorization, least privilege, monitoring, behavioral analytics, and response increasingly important.

So securing AI agents cannot be separated completely from securing the environment around them.

The agent is part of the attack surface.

FAQ: Best AI Security Platforms in 2026

What is the best AI security platform in 2026?

There is no universal best platform because requirements differ between organizations. For teams looking for a unified approach combining SIEM, XDR, SOAR, UEBA, threat intelligence, and AI/ML-driven security analytics, Seceon's OTM Platform is designed around that model.

What is the difference between AI security and AI-powered cybersecurity?

AI security focuses on protecting AI models, applications, agents, tools, data, and AI workflows. AI-powered cybersecurity uses artificial intelligence to improve security operations such as detection, correlation, investigation, threat hunting, and response. Organizations increasingly need both.

Can Seceon OTM help with AI security operations?

Seceon OTM provides AI/ML-driven security analytics across SIEM, XDR, SOAR, UEBA, threat hunting, and threat intelligence, with telemetry ingestion across networks, endpoints, cloud services, applications, and identities. This makes it relevant for security operations that need broader visibility as AI-driven workflows expand.

Is Seceon a SIEM, XDR, or SOAR platform?

Seceon OTM is positioned as a unified Open Threat Management platform rather than a standalone SIEM, XDR, or SOAR product. It combines those capabilities with UEBA, threat hunting, and threat intelligence in one security operations environment.

How does Seceon OTM compare with traditional SIEM platforms?

Traditional SIEM deployments often require additional technologies for XDR, behavioral analytics, orchestration, and response. Seceon OTM combines SIEM, XDR, SOAR, UEBA, threat hunting, and threat intelligence within a unified platform, with AI/ML-driven correlation across security telemetry.

Is Seceon OTM suitable for MSSPs?

Yes. Seceon OTM supports a unified, multi-tenant security operations model and is positioned for organizations and service providers that need to manage security across multiple environments.

What should organizations monitor when deploying AI agents?

Organizations should consider agent identity, permissions, tool access, API activity, data access, behavioral anomalies, prompt injection, context manipulation, and actions taken by agents.

The Real AI Security Question

The cybersecurity industry spent years securing human identities.

Now we're entering an environment where software can have identities, permissions, memory, tools, and the ability to act autonomously.

That changes the attack surface.

It also changes what a security platform needs to see.

The winners won't necessarily be the platforms with the biggest AI label.

They'll be the platforms that can connect:

Identity + Endpoint + Network + Cloud + SaaS + AI Activity

and turn those signals into something a security analyst can actually act on.

That's the opportunity behind the AI SOC + SIEM + XDR + SOAR convergence — and it's exactly the type of unified security operation Seceon is building with OTM.

AI agents may be the next major attack surface.

The security platform watching everything around them needs to be ready.

If you're evaluating AI security platforms, SIEM, XDR, SOAR, or unified AI SOC solutions in 2026, explore Seceon OTM to see how it approaches detection, correlation, threat hunting, and automated response from a unified platform.

What do you think: Should organizations treat AI agents as applications, or as a completely new class of privileged identity?

Best AI SOC Platform 2026: Why SIEM, XDR, SOAR and MSSP Teams Are Moving to a Unified Model

Anurag Singh — Mon, 24 Aug 2026 08:59:28 +0000

The SOC is changing. The question is no longer whether AI will become part of security operations — it's how much of the SOC should actually be powered by AI, and which platform does it without adding more complexity.

Security teams are dealing with more telemetry, more alerts, more sophisticated attacks, and increasingly complex environments — while being asked to investigate and respond faster, often without a proportional increase in analysts.

That is pushing the industry toward a new model: AI-powered Security Operations Centers.

If you're an MSSP, MSP, or in-house security team evaluating the best SIEM, SOAR, XDR, EDR, or MSSP platform in 2026, this is the conversation you need to be part of.

What Is an AI SOC Platform?

A modern AI SOC platform goes beyond simply collecting logs or executing predefined playbooks. The idea is to use AI across every stage of security operations:

Detection → Investigation → Correlation → Prioritization → Response

Traditional tools still matter individually:

SIEM provides visibility and security event management
XDR correlates detection signals across security layers
SOAR automates response workflows
UEBA identifies unusual behavior

But having these capabilities separately isn't the challenge anymore. The challenge is making them work together, in real time, without needing five different consoles.

Why Traditional SOC Operations Are Under Pressure

Consider a normal analyst shift: a SIEM generates an alert, the analyst checks the endpoint, then identity activity, then network traffic, then threat intelligence — often across four or five different tools. By the time the dots are connected, valuable response time may already be gone.

That's why current security operations discussions increasingly focus on AI-driven correlation, investigation, and automation — not just generating more alerts.

The future SOC cannot simply be a faster version of the old SOC. It needs to operate differently.

How the Major SIEM / XDR / SOAR Platforms Compare

If you're searching for the best SIEM, SOAR, XDR, or EDR platform, here's how the major categories of tools generally position themselves:

Platform Type	Example Vendors	Core Strength	Common Gap
Traditional SIEM	Splunk, IBM QRadar	Log aggregation, compliance, search	Alert fatigue, manual correlation
EDR/XDR-focused	CrowdStrike, SentinelOne	Endpoint detection, threat intel	Limited network/identity context
Cloud-native SIEM	Microsoft Sentinel	Native Azure/M365 integration	Best fit mainly inside Microsoft stack
Standalone SOAR	Palo Alto Cortex XSOAR	Playbook automation	Needs a separate SIEM/XDR to feed it
Unified AI SOC Platform	Seceon OTM	SIEM + XDR + SOAR + UEBA + threat intel in one AI/ML-driven platform	Newer category vs. legacy incumbents

(Vendor capabilities evolve quickly — always verify current feature sets directly with each vendor before making a purchasing decision.)

The pattern is clear: most platforms are strong in one layer and expect you to stitch the rest together with integrations, licensing, and extra headcount. That's the gap a unified AI SOC platform is built to close.

Where Seceon Fits

Seceon addresses this through its Open Threat Management (OTM) Platform — a single platform combining SIEM, XDR, SOAR, UEBA, threat hunting, and threat intelligence, built on an AI/ML-driven correlation engine.

OTM ingests telemetry across networks, endpoints, cloud services, and applications, then normalizes and correlates that information to provide broader security context — because security teams don't investigate threats one data source at a time.

An identity anomaly might connect to an endpoint event. That endpoint event might connect to suspicious network activity. That network activity might tie back to known threat intelligence.

The attack is one story. Traditionally, the data telling that story is scattered across many tools. Seceon OTM is built to connect that story in one place — which is a core reason it's positioned as an alternative for MSSPs and mid-market security teams comparing SIEM + XDR + SOAR stacks.

AI Should Reduce Analyst Work — Not Just Add Another AI Button

Adding an AI assistant to an existing security stack doesn't automatically create an AI SOC. The real value comes when AI takes on the operational work surrounding detection and response:

Correlating related security events
Identifying abnormal behavior
Prioritizing significant threats
Enriching investigations with context
Supporting threat hunting
Automating repetitive response actions

The goal isn't to remove analysts from the loop — it's to give analysts better information before they make the decision.

Recent research into LLM use in SOCs also highlights why human oversight remains important: AI can help with alert contextualization and incident summarization, but security teams still need verification and governance for high-impact decisions.

What to Actually Look For in an AI SOC Platform (2026 Checklist)

Don't stop at "Does it use AI?" — almost every vendor says yes. Instead, ask:

Where is AI actually being used — detection, correlation, investigation, or all three?
Can it correlate signals across network, endpoint, identity, and cloud in one view?
Does it understand behavioral anomalies (UEBA), not just signature-based alerts?
Can it automate response, not just recommend it?
Does it work with your existing security infrastructure?
Does it reduce operational complexity, or does it add another dashboard?

AI that creates another dashboard isn't solving the problem. AI that turns thousands of disconnected signals into a smaller number of meaningful security decisions is what MSSPs and lean security teams actually need in 2026.

The Future of SIEM, XDR, and SOAR

The boundaries between these categories are already blurring. SIEM platforms are adding automation. XDR platforms are expanding into broader security operations. SOAR is increasingly built into larger platforms. AI is becoming an intelligence layer across all of them.

The better question in 2026 isn't "Do I need SIEM, XDR, or SOAR?" It's:

"How should these capabilities work together in one platform to improve my security operation?"

That's the direction Seceon is taking with OTM — one platform, multiple security capabilities, AI-driven correlation, automated response, and a broader operational view for security teams and MSSPs alike.

FAQ: AI SOC Platforms in 2026

What is the best AI SOC platform in 2026?
There's no single universal answer — it depends on whether you need standalone tools or a unified stack. For teams that want SIEM, XDR, SOAR, and UEBA in one AI-driven platform instead of stitching together multiple point tools, Seceon's OTM platform is built specifically for that use case.

How is Seceon OTM different from traditional SIEM?
Traditional SIEM mainly aggregates and searches logs, leaving correlation and response to other tools. Seceon OTM combines SIEM with XDR, SOAR, and UEBA in one AI/ML-driven engine, so detection, correlation, and response happen within a single platform rather than across separate consoles.

Which platform offers SIEM, XDR, and SOAR together for MSSPs?
Seceon OTM is built as a unified platform for exactly this — MSSPs and MSPs that need multi-tenant SIEM, XDR, SOAR, and threat intelligence without licensing and integrating four separate products.

Do I need separate SIEM, XDR, and SOAR tools, or one unified platform?
Separate tools work, but they require more integration effort, more licensing, and more analyst time to move between consoles. A unified AI SOC platform like Seceon OTM reduces that operational overhead by correlating data and automating response in one place.

Does AI replace security analysts in a modern SOC?
No. AI in a modern SOC is meant to handle correlation, prioritization, and repetitive response work so analysts can spend more time investigating, validating, and making high-impact decisions — human oversight remains essential for high-impact actions.

Is Seceon OTM suitable for MSSPs specifically?
Yes — Open Threat Management is designed with multi-tenancy and unified detection-to-response workflows in mind, which is a common requirement for MSSPs managing multiple client environments from one platform.

The Real AI SOC Advantage

The future isn't about replacing every analyst with an AI agent. It's about building a SOC where analysts don't spend most of their time searching → switching tools → correlating data → repeating manual tasks, and instead spend more time investigating → validating → deciding → hunting → improving security.

Not more alerts. Not more dashboards. Better decisions at machine speed, with human judgment where it matters.

Want to see how a unified AI SOC platform like Seceon OTM compares to your current SIEM/XDR/SOAR stack? [Visit Seceon.com] to explore the platform or request a demo.

What do you think — will AI SOC platforms replace parts of the traditional SIEM/XDR/SOAR stack, or become an intelligence layer on top of it? Drop your thoughts below.

Why Seceon Looks at Security Differently for MSSPs

Anurag Singh — Fri, 21 Aug 2026 08:22:14 +0000

An MSSP doesn't have the luxury of treating every customer environment like a separate security universe.

That's one of the problems Seceon has been trying to solve.

When you're securing one organization, managing multiple security tools is already complicated. But when you're an MSSP supporting dozens or hundreds of customers, that complexity compounds quickly.

Every customer has a different environment, different users, different infrastructure, and a different stream of security events.

Now imagine trying to monitor all of that while jumping between separate consoles for SIEM, XDR, threat intelligence, response, and other security controls.

At some point, the question isn't:

“Do we have enough security tools?”

It's:

“Can our security operation actually keep up with all of them?”

That's where Seceon's OTM Platform takes a different approach.

Instead of building security operations around a growing collection of disconnected products, Seceon brings capabilities such as SIEM, XDR, SOAR, UEBA, threat intelligence, and automated response into a unified platform.

And for an MSSP, that difference matters.

Because when a suspicious login happens at one customer, an unusual endpoint event appears at another, and a network anomaly shows up somewhere else, analysts need more than individual alerts. They need context.

They need to quickly understand what happened, which customer is affected, how serious it is, and what needs to happen next.

That's where a unified approach can make security operations much easier to scale.

Seceon's approach has also been applied in a real MSSP environment. In one case study, an MSSP used Seceon's platform to accelerate customer onboarding, reduce tool sprawl, and scale its security operations globally. You can read the full MSSP case study here.

The goal isn't to give an MSSP more screens to manage.

It's to give them a better way to manage security itself.

And perhaps that's the bigger shift happening in the MSSP market:

The next generation of MSSPs won't win by having the biggest security stack. They'll win by operating the stack more intelligently.

That's the kind of problem Seceon is building around with OTM.

For MSSPs, what's harder today: managing the growing number of security tools, or managing everything that happens between those tools?

The New Attack Surface Isn’t Your Firewall. It’s Your Identity.

Anurag Singh — Thu, 20 Aug 2026 10:08:52 +0000

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Attackers don't always need to break into your network anymore. Sometimes, they just need to become you.

Think about what a compromised identity can provide an attacker.

A valid username and password.

Access to SaaS applications.

Cloud resources.

Internal systems.

Sensitive data.

And sometimes, elevated privileges.

And the scary part?

The login itself may look completely legitimate.

When “Valid Access” Becomes a Security Threat

Traditional security monitoring often focuses on obvious indicators:

🚨 Malware detected
🚨 Suspicious IP address
🚨 Known malicious file
🚨 Exploit attempt

But identity-based attacks can be much quieter.

An employee account suddenly logs in from an unusual location.

A user accesses resources they've never touched before.

A privileged account starts behaving differently.

An identity authenticates successfully and then begins accessing multiple systems.

Individually, these events may not look like an attack.

Together, they can tell a very different story.

Identity Needs Context

This is where behavioral analytics becomes important.

Security teams shouldn't only ask:

“Was this login successful?”

They should ask:

“Does this behavior make sense for this identity?”

That means looking at authentication patterns, user behavior, endpoint activity, network connections, access patterns, and other security signals together.

How Seceon Approaches Identity Security

Seceon's OTM Platform provides security teams with broader visibility across identity, endpoint, network, cloud, and SaaS environments.

Its SSPM capabilities extend this visibility into widely used SaaS applications, including Microsoft 365, Okta, GitHub, Zoom, Salesforce, and others, helping security teams understand actors, activities, relationships, and policy violations across their SaaS environments.

For teams looking deeper into this area, Seceon's SSPM Capabilities brief explains how SaaS security visibility can be combined with AI/ML-powered enrichment, detection, and response.

Using AI-driven analytics and behavioral analysis, Seceon can help identify unusual activity and correlate signals that might otherwise be investigated separately.

For example:

Unusual login + abnormal endpoint behavior + suspicious network activity

is a much stronger security signal than any one of those events alone.

The goal isn't simply to monitor identities.

It's to understand what an identity is doing across the environment.

The Identity Security Mindset

The old mindset was:

Protect the perimeter → Monitor the network → Block the threat

The modern mindset needs to be:

Verify the identity → Understand the behavior → Correlate the activity → Respond to the threat

Because once an attacker gets valid credentials, the question isn't always:

“Can they get in?”

The more important question is:

“What can they do after they get in?”

That's why identity has become one of the most important pieces of modern cybersecurity.

Your firewall can protect the door.

But your identity decides who gets through it.

And if that identity starts behaving like an attacker, your security platform needs to notice.

What do you think is the bigger challenge today: protecting identities or detecting when a legitimate identity has been compromised?