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Your GuardDuty Alert Isn't The End, It's Where Investigation Begins.

adi271001 — Mon, 27 Jul 2026 00:18:24 +0000

Using Amazon Detective to uncover the full story behind an AWS Security Event.

Introduction
The time is 2:13 AM.

A high-severity alert from your Security Operations Center (SOC) causes your phone to vibrate. An UnauthorizedAccess:IAMUser/ConsoleLogin discovery has been found in your AWS environment by Amazon GuardDuty.

You briefly question if it's just another false positive. After all, not every security discovery results in a breach, yet cloud environments produce thousands of events every minute.

The GuardDuty console is opened.The discovery offers a detection, which is precisely what it is intended to offer. It informs you that an IAM user was able to successfully authenticate from a strange place that doesn't correspond with the account's past activity. The action is suspicious enough to be investigated right now.

But almost instantly more questions arise than answers.

Was this login actually malicious?
Has the IAM user been compromised?
What happened immediately after the login?
Did the attacker access sensitive resources?
Were AWS Secrets Manager secrets retrieved?
Was data exfiltrated?
Has the attacker established persistence?
Is the incident still active?

When a security engineer receives an alert, they always ask these questions.

Sadly, the alert itself is unable to respond to them.
One of the most common misconceptions about cloud security nowadays is this.

Many teams think that the most challenging aspect of security is threat detection. In actuality, detection is just the first step. The true difficulty is figuring out an incident's entire extent fast enough to make wise decisions.

An alert informs you of an event. A probe reveals the truth. As businesses continue to move workloads to AWS, this distinction becomes more crucial. Contemporary cloud infrastructures are extremely interconnected, distributed, and dynamic. Numerous AWS services can communicate with a single IAM role. Hundreds of API requests can be made in a few minutes using a hacked credential.

In order to manually investigate these events, it is frequently necessary to switch between CloudTrail logs, GuardDuty findings, IAM policies, VPC Flow Logs, and several AWS terminals while attempting to put together a timeline from disparate bits of information.

This not only takes a lot of time, but it also causes a major delay in incident response. Amazon Detective was created specifically to address this issue. After an alert has been raised, Amazon Detective assists security teams in answering the most important questions rather than creating new alerts.

It automatically establishes connections between identities and resources, correlates data from AWS services, shows activity over time, and gives investigators the background information they need to comprehend what actually occurred.

Rather than asking, "Where do I start?", "How far did this incident spread?" is a question that investigators can start asking right away.

Detection Is Only Half The Battle
For good reason, Amazon GuardDuty has grown to be one of the most popular AWS security services. To find suspect activity, it continuously examines AWS activity using machine learning, anomaly detection, and AWS threat intelligence.

GuardDuty offers useful indications that assist enterprises in identifying threats early on, whether they are related to bitcoin mining, strange API activity, compromised credentials, or reconnaissance activities.

GuardDuty, however, purposefully concentrates on detection. Think about a typical result.

IAMUser/ConsoleLogin Unauthorized Access Severity: High

This seems frightening at first. However, this discovery raises many more problems than it provides answers from the standpoint of incident response. Assume you are the analyst in charge of looking into this warning.

Your investigation immediately begins with questions like these:

Has this IAM user ever logged in from this location before?
Were MFA protections enabled?
Which AWS Region was accessed?
What API calls followed the login?
Did the user enumerate S3 buckets?
Were secrets retrieved?
Were new IAM users or access keys created?
Were EC2 instances launched?
Was CloudTrail modified?

GuardDuty cannot provide the answers to these queries on its own.

Rather, analysts typically alternate between several services:

AWS CloudTrail
IAM
Amazon S3
VPC Flow Logs
Route 53 Resolver logs
Security Hub
GuardDuty

One element of the puzzle is provided by each service. The difficulty is putting those parts together before important research time is lost. This procedure is doable for seasoned analyzers. It can easily become too much for smaller security teams or companies with little experience with cloud security. An attacker may stay active for an additional minute for every minute spent manually searching logs.

Meet Amazon Detective
This is where the discussion gets altered by Amazon Detective. Considering the distinction between a security camera and a detective looking into a crime is one of the simplest methods to comprehend Detective.

Events are captured via a security camera. The story is recreated by a detective. In a similar vein, GuardDuty alerts you to unusual conduct.

Amazon Detective clarifies:

who carried out the task.
which AWS resources were used.
what took place prior to the warning.
What followed?
The connections between resources.
if the behavior is out of the ordinary.
How the event changed over time.

Detective automatically arranges and correlates data into an investigation-friendly structure rather than manually gathering evidence from many AWS services.

To create a thorough behavioral graph of your AWS environment, it continuously ingests telemetry from services like AWS CloudTrail, Amazon GuardDuty, VPC Flow Logs, and Route 53 Resolver DNS logs.

This eliminates the need for investigators to manually piece together evidence by allowing them to switch between identities, IP addresses, AWS resources, API calls, and timings.

Analysts can concentrate on comprehending the attack instead of wasting important time gathering data. It can seem like a small difference. In actuality, it can significantly shorten the duration of an investigation.

Our Investigation Begins
Let's examine a simulated, yet realistic, AWS security event to gain a better understanding of how Amazon Detective fits into a contemporary incident response workflow.

Consider yourself a member of a cloud security team that is in charge of safeguarding a production AWS account that houses many applications that interact with customers.

Before GuardDuty delivers a high-severity discovery concerning an IAM user named finance-automation, everything seems okay. Nothing about the account itself appears out of the ordinary at first glance. The IAM user has been in charge of automating financial reporting procedures for several months. Today's login, however, came from a nation in which the company has no workers, contractors, or commercial activities.

That prompts suspicion right away. Several API calls start to show up throughout the environment a few seconds after the login. Some appear innocuous. Some people don't. Finding out if the login was suspicious is no longer your goal. Understanding the attacker's intent is your goal.
Did they investigate the surroundings? Are you looking for secrets? Getting ready to persevere? Or was this just an automatic mechanism acting strangely? This is the real start of our inquiry.

Following The Evidence With Amazon Detective
We don't know much at this time. We've been notified by GuardDuty that an IAM user logged in from an odd place, but we're still unsure if this is just an employee traveling abroad or if the account has been compromised.

In cloud security, this uncertainty is typical. Each warning is a possibility rather than a conclusion. While waiting too long can enable an attacker to get farther into the environment, jumping to containment too soon can interfere with legitimate company operations.

Our objective is straightforward: Prior to developing assumptions, gather evidence. We launch Amazon Detective rather than manually searching CloudTrail, IAM, VPC Flow Logs, and many AWS consoles.In contrast to conventional logging tools, Detective starts narrating a story right away.

Starting With Entity Profile
An entity profile for the IAM user that caused the GuardDuty discovery is among the first items Detective displays. An entity profile can be compared to a dossier. Detective compiles all of its knowledge about a specific identity into a single screen rather than displaying discrete log entries.

The profile for our inquiry comprises:

Recent action involving sign-ins
Findings related to GuardDuty
Access to AWS Regions
IP addresses of the source
History of API calls
Associated AWS resources
Past patterns of behavior

An intriguing information emerges in a matter of seconds.Over the past six months, the finance-automation IAM user has regularly accessed AWS from Sydney, Australia. During regular business hours, all successful logins came from the same corporate IP range.

The login for today reveals a different picture. The source IP comes from a different nation where the company doesn't have any offices, staff, or anticipated business activity. A breach might not be confirmed by a single odd login. However, it undoubtedly warrants further investigation.

Looking Beyond Login
Rarely is authentication the attack. It's just the start. Credentials are rarely compromised by attackers merely to admire the AWS Management Console.

They nearly always have one of the following goals in mind:

Find resources
Access private information
Increase privileges
Develop perseverance
Hide their tracks.

This implies that the most crucial query is now: What transpired right after logging in? Thankfully, Detective creates an API activity timeline automatically.

We witness the following sequence in a matter of seconds.

02:13:17  Successful Console Login
02:13:34  ListBuckets
02:13:51  GetCallerIdentity
02:14:09  ListSecrets
02:14:28  GetSecretValue
02:15:11  DescribeInstances
02:15:52  ListRoles
02:16:40  PassRole
02:17:03  RunInstances

None of these incidents seem very concerning when viewed separately. But taken as a whole, they show something far more alarming. This login is no longer questionable. It's starting to look like a planned attack sequence.

Recognizing The Attacker's Playbook
The ability for analysts to comprehend activity in context rather than in isolation is one of Amazon Detective's greatest benefits.

Let's look at the chronology. The attacker lists each S3 bucket as soon as they log in. Why? because the location of valuable data is rarely known to attackers. The first step is generally always enumeration.

They then make a call to GetCallerIdentity.This API verifies the rights linked to the hacked credentials and lets them know exactly who they are inside the AWS account. The attacker is now aware of their access. Even more fascinating is the next task. They make a query to AWS Secrets Manager.

Because secret stores contain credentials, API keys, database passwords, certificates, and tokens that access extra resources, current attackers often target these instead of databases directly.

When a GetSecretValue request is successful, the incident's severity is immediately increased. The assailant is no longer investigating. They have started gathering private data. Shortly after, they list all of the EC2 instances before looking at the IAM roles that are available.

Lastly, they try to use an existing IAM role to start a new EC2 instance. Every security engineer should feel uneasy about that action right away. One popular method of establishing persistence or establishing a foothold for future action is to launch compute resources utilizing an existing privileged role.

In less than four minutes, the entire sequence takes place. It would be necessary to manually scan hundreds or even thousands of CloudTrail records in order to reconstruct these occurrences without Detective's chronology.

Visualizing Relationships Instead Of Reading Logs
Logs are a great way to document happenings. Their comprehension of relationships is appalling. This is where Amazon Detective really stands out. Detective creates an entity graph that illustrates the connections between AWS identities, resources, and network activities rather than exhibiting discrete occurrences.

Instead than inquiring, "What happened next?" "What else is connected?" is the first question we ask. Detective instantly provides more context by choosing the EC2 instance. The instance has never been started before.

It is present in a subnet used for production. Multiple S3 buckets and Secrets Manager secrets can be accessed by its IAM role. Our perception of the incident is significantly altered by just one visualization. The assailant wasn't just playing around. They were trying to inherit a more affluent identity.

Pivoting Into CloudTrail
Analysts want accurate evidence at some point in every investigation. Here's where CloudTrail comes in very handy. By enabling investigators to go straight into the underlying API activity, Detective greatly simplifies this process. We are directed straight to the pertinent CloudTrail records rather than having to manually sift through millions of events.

We can now verify information like:

Precise timestamps
Request specifications
IP addresses of the source
Strings for user agents
Regions of AWS
Request IDs
Error messages

This degree of specificity aids in differentiating between successful and unsuccessful endeavors.For instance, Detective demonstrates that although the attacker was able to list IAM roles, an IAM policy constraint prevented them from passing a highly privileged role.

We learn something crucial from that one unsuccessful API call. The attacker was making a concerted effort to get further rights. Thankfully, such escalation was avoided thanks to the organization's least-privilege IAM policy. Attacks are not always prevented by security measures. At times, they only compel attackers to disclose their motivations.

Identifying The Blast Radius
During any crisis, one of the most challenging questions is: What is the extent of the damage? Manually determining the solution frequently entails examining dozens of services one at a time. By displaying each AWS resource linked to the compromised identity throughout the investigation window, Detective expedites this procedure.

We find that the attacker engaged with the following in our simulation:

Three buckets on Amazon S3
There are two manager secrets.
One instance of EC2
Seven CloudTrail API types and four IAM roles

It's interesting to note that there is no proof that DynamoDB tables, Lambda functions, or Amazon RDS databases were accessed. The possible blast radius is immediately decreased as a result. Detective verifies that no new IAM users or access keys were generated, which is equally crucial. It seems that persistence is ineffective. Responders can prioritize containment based on facts rather than conjecture thanks to these insights.

Turning Evidence Into Action
Compared to the initial GuardDuty alert, our study has now provided a far clearer picture. A confirmed credential compromise, reconnaissance, secret discovery, and an attempt at privilege escalation have all followed what at first seemed to be a strange login.

We arrived to this conclusion in minutes rather than hours since Detective correlated metrics across several AWS services.Asking what happened is no longer the next step.

It's choosing how to react. Should the IAM credentials be cancelled right away? Which secrets should be rotated? Was any private information compromised? Are other accounts impacted as well? The change from investigation to incident response is signaled by these questions.

From Investigation To Response
The GuardDuty discovery is now more than just a dashboard alert. We have recreated the events, determined the compromised identity, seen reconnaissance activities, verified access to sensitive resources, and discovered an attempted privilege escalation using Amazon Detective.

More significantly, all of this has been done with evidence rather than conjecture. This distinction is crucial.

Acting too soon without fully comprehending the scope of an assault is one of the major mistakes businesses make when responding to incidents. Disabling an IAM user right away might halt the current activity, but the attacker might still have access if temporary credentials have already been issued or if there are other persistence measures in place.

Making wise decisions is more important for effective incident response than merely responding to alarms. Now that we know what went wrong, we can concentrate on stopping the attack, recovering safely, and making sure it doesn't happen again.

Containing The Incident
In our simulated research, the attacker was able to launch compute resources using an existing IAM role, enumerate cloud resources, access secrets, and successfully authenticate using compromised IAM credentials.

IAM controls prevented privilege escalation, but the inquiry found sufficient evidence to support rapid containment.Revoking the compromised credentials is the first step. Passwords should be changed, any temporary AWS STS sessions should be canceled, and any current access keys connected to the IAM user should be disabled. Credential hygiene comes next.

Every secret observed during the investigation should be regarded as potentially compromised since the attacker was able to get access to AWS Secrets Manager. The organization's incident response protocols should be followed when rotating database passwords, API tokens, third-party credentials, and application secrets.

Many teams fail to learn this crucial lesson. You should presume that an attacker copied a secret if they saw it. Rotation is frequently far faster than attempting to ascertain whether exfiltration took place.

Validating The Blast Radius
One aspect of the response is containment. Determining whether the compromise extended beyond the initial identity is the next goal. Investigators should use Amazon Detective in conjunction with AWS

CloudTrail and AWS Security Hub to confirm whether the attacker:

added more IAM roles or users
created fresh access keys
IAM trust relationships that have been modified
created EventBridge rules or scheduled tasks
New Lambda functions were deployed.
Unexpected EC2 instances were launched
S3 bucket policies that have been modified
Disabled security or logging features

Thankfully, no indication of successful persistence was discovered during our study. Every encounter, though, should end with confirmation rather than hope. The lack of further activity is only significant when it is backed up by data.

Lessons From The Investigation
The chance to get better is one of the most beneficial results of any security incident. Our research reveals a number of lessons that go beyond Amazon Detective. First, visibility is more important than volume.

Every day, massive volumes of telemetry are produced by cloud systems. Every API request is recorded by CloudTrail, network traffic is captured by VPC Flow Logs, GuardDuty reports discoveries, and Security Hub compiles warnings from several services.

If this information cannot be integrated into a cohesive narrative, it is all useless. Because it turns discrete incidents into significant connections, Amazon Detective succeeds.

It offers context, which significantly reduces the amount of time needed for investigations, rather than requiring analysts to manually correlate thousands of log entries.

The second lesson is that the cloud's major attack surface is still identity. Cloud assaults often start with compromised credentials, in contrast to traditional on-premises setups where attackers frequently target network perimeters.

Unless businesses monitor behavior instead of just authentication, once an attacker establishes a trusted identity, all subsequent actions appear legal. This supports the continued use of IAM, multi-factor authentication, short-lived credentials, and least privilege as fundamental security measures.

Lastly, the probe serves as a reminder that speed is important. An attacker can continue to operate for an additional minute for each minute spent manually searching logs. The results of incident response are immediately improved by cutting down on investigative time.

Building A Stronger Investigation Workflow
When Amazon Detective is integrated into a larger AWS security plan as opposed to functioning independently, it yields the best results. Every service has a certain function.

Suspicious activity is identified by GuardDuty. Findings from all AWS services are centralized via Security Hub. Investigative context is provided by the detective. CloudTrail provides thorough evidence for forensic examination.

Then, when necessary, automation technologies like Amazon EventBridge, AWS Lambda, or AWS Systems Manager can carry out specified containment actions. Cloud security cannot be resolved by a single service. Their cooperation is what gives them strength.

Best Practices For Using Amazon Detective
Businesses thinking in using Amazon Detective should consider more than just enabling the service.

To increase its efficacy:

Enable thorough event logging for AWS CloudTrail in all regions.
Activate Amazon GuardDuty across the entire enterprise to guarantee reliable threat identification.
Utilize AWS Security Hub to compile results for a cohesive security perspective.
Review unneeded permissions on a regular basis and implement least-privilege IAM policies.
Use multi-factor authentication (MFA) to safeguard privileged identities.
Rotate secrets kept in AWS Secrets Manager on a regular basis.
Create and test incident response playbooks so that responders are prepared to take appropriate action when a detective finds suspicious activity.
To verify investigation procedures and team preparedness, conduct simulated security drills.

Effective crisis response involves more than just technology. Equally crucial is preparation.

Looking Ahead
Cloud environments are still developing at a remarkable rate. Infrastructure is becoming more and more transient. In a matter of seconds, serverless workloads appear and vanish. Applications driven by AI use AWS services on their own. Organizations frequently support thousands of identities and millions of API calls daily while operating across various accounts and regions.

Scaling traditional research approaches becomes more challenging as situations become more dynamic. Gathering more logs is not the way of the future for cloud security. It involves deriving significant context from the existing telemetry.

A significant move toward context-driven investigations is represented by services like Amazon Detective, which enable security teams to spend more time comprehending attacker behavior and less time looking for evidence.

The adoption of autonomous systems, AI agents, and increasingly sophisticated cloud infrastructures by enterprises will only make this trend more significant.

Conclusion
This investigation's most significant lesson is shockingly straightforward. An incident is not the same as an alert. A GuardDuty finding indicates that anything out of the ordinary happened.

It doesn't explain why it occurred, how far it spread, or what the attacker was attempting to achieve. That void is filled by Amazon Detective. Detective helps security teams go beyond detection and into understanding by establishing entity links, correlating telemetry from AWS services, and presenting investigations through timelines and behavioral analysis.

That distinction is crucial for cloud defenders. The quantity of notifications generated is not a measure of security. It is determined by how fast teams can look into, comprehend, and react when those signals are most important.

Don't consider that the end of the tale the next time GuardDuty alerts you to questionable activities. Consider it the opening page. Because each alert informs you about an event. You can learn the remainder of the tale using Amazon Detective.

Shadow AI in AWS: Detecting and Governing Unauthorized AI usage in 2026

adi271001 — Sun, 28 Jun 2026 10:45:22 +0000

AI adoption is accelerating across enterprises, but not always under the watchful eye of security teams. As organizations embrace generative AI, a new challenge has emerged which is the Shadow AI. Much like shadow IT from before it, Shadow AI introduces visibility, governance, and data protection concerns that traditional security programs were never designed to address.

Introduction
Generative AI has transformed the way organizations build software, analyze data and automate workflows. Developers rely on coding assistants to accelerate delivery, analysts use Large Language Models (LLMs) to summarize reports and, business teams increasingly integrate AI into daily operations.

This rapid adoption has undoubtedly improved productivity but it has also created a new category of risk. Across many organizations, employees are introducing AI tools without the knowledge or approval of security teams. Public chatbots, browser extensions, autonomous agents, and third party AI APIs are becoming a part of everyday workflows, often outside established governance processes.This phenomenon is commonly referred to as Shadow AI, is quickly emerging as one of the most important cybersecurity challenges of 2026.

Unlike traditional software, AI systems thrive on information. Their value comes from consuming context, learning patterns and generating responses based on data. Unfortunately the same characteristic that makes AI useful also makes it dangerous!!. Sensitive information can be exposed unintentionally, proprietary knowledge may leave organizational boundaries, and AI generated outputs can introduce entirely new attack surfaces.

The challenge facing security teams is no longer simply protecting infrastructure. It is understanding and governing the AI systems that employees are already using.

Understanding Shadow AI
Shadow AI refers to the use of artificial intelligence tools and services that operate outside approved organizational policies and governance frameworks. Similar to the rise of Shadow IT more than a decade ago, Shadow AI emerges when employees seek productivity gains faster than security processes can adapt.

A developer troubleshooting an issue might paste application logs into a public chatbot. A data analyst could upload spreadsheets containing customer information to an external AI service. A project team may experiment with autonomous agents that interact with cloud environments, git repositories, or collaboration platforms without undergoing any security review.

Most of these actions are not malicious. They are driven by the convinience and the desire to work more efficiently. However good intentions do not eliminate risk. What makes Shadow AI particularly challenging is that it often remains invisible. Security teams cannot protect assets they do not know exist, and organizations cannot govern technologies they cannot see.

Why Shadow AI Has Become A Security Concern
The concern surrounding the Shadow AI is not the existence of AI itself. It is the uncontrolled movement of information. Unlike traditional applications, AI systems actively consume and process large volumes of contextual data. This can include source code, credentials, internal documentation, customer records, architectural diagrams or confidential business information.

A seemingly harmless interaction with an AI assistant can unintentionally expose secrets that were never meant to leave an organization. Furthermore, compliance requirements such as ISO27001, PCI-DSS, HIPAA and SOC2 place strict obligations on how sensitive information is handled. Employees using unauthorized AI services may unknowingly violate these requirements, creating both security and regulatory challenges.

Another growing concern is equality of AI generated outputs. Developers increasingly rely on AI generated code, yet these outputs can contain insecure configurations, vulnerabale dependencies, hardocded credentials, or poor security practices. The result is an expanding attack surface that traditional vulnerability management approaches struggle to address.

The Rise of Shadow AI in AWS Environments
AWS customers are embracing artificial intelligence at an unprecedent pace. Services such as Amazon Bedrock, Amazon Q Developer, Amazon Sagemaker, and bedrock knowledge bases are enabling organizations to build sophisticated AI powered applications while maintaining control over data and governance.

At the same time, employees are experimenting with public AI platforms and third party services that exist outside approved environments. This creates a hybrid landscape where trusted AI capabilities, coexist with unmanaged tools.

Consider a developer using Amazon Q Developer for code generation while simultaneously consulting a public chatbot to debug an issue. During the troubleshooting process, snippets of infrastructure as a code templates containing IAM policies or API keys maybe shared externally. What begins as an attempt to increase productivity can quickly become a data exposure incident.

The challenge for security teams is not preventing AI adoption. Innovation cannot and should not be stopped. The real objective is enabling AI adoption securely.

Common Risks Associated With Shadow AI
One of the most significant risks associated with Shadow AI is data leakage. Employees frequently interact with AI systems using real world information. credentials, customer records, infrastructure details and proprietary algorithms may all be shared inadvertently.

Source code exposure represents another growing concer, infrastructure templates, kubernetes manifests and application code often contain valuable information for attackers. Even without exploiting a vulnerability, exposed architectural knowledge can provide a roadmap for future attacks.

The emergence of autonomous AI agents introduces an additional layer of complexity. Modern agents can interact with Github repositories, Jira tickets, Slack channels and AWS resources. If these agents are granted excessive permissions, they may retrieve secrets, modify infrastructure or perform actions that exceed their intended purpose.

Prompt injection attacks further complicate the picture. Researchers have demonstrated how malicious instructions embedded within documents or external content can influence AI systems and manipulate their behavior. As AI agents become increasingly autonomous, these attacks may evolve into a significant threat vector.

Improving Visibility With AWS Services
Visibility remains one of the most effective defenses against Shadow AI. Fortunately, AWS provides several services that can help organizations improve awareness and strengthen governance.

Amazon Macie enables organizations to discover and classify sensitive data stored within Amazon S3. By automatically identifying personally identifiable information, financial records and credentials Macie provides valuable insight into where sensitive information resides.

AWS CloudTrail plays a central role in understanding activity across AWS environments. By recording API calls and user actions, CloudTrail enables security teams to investigate unusual behavior and understand who performed the specific operation and where they occured.

Amazon GuardDuty compliments this visibility by continuously monitoring for suspicious activity. Through machine learning and threat intelligence, GuardDuty can detect compromised credentials, anomalous API calls and unusual access patterns that may indicate misuse.

AWS Security Hub provides a centralized view of findings generated by GuardDuty, Macie, Inspector and other services. Rather than navigating multiple consoles, security teams can correlate findings and gain a more comprehensive understanding of their security posture. Together these services create a foundation for detecting risks associated with Shadow AI and strengthing organizational awareness.

Securing Generative AI With AWS Bedrock Guardrails
As organizations increasingly adopt generative AI, governance mechanisms become essential. Amazon Bedrock Guardrails provide a powerful capability for implementing responsible AI controls. Organizations can define policies that filter harmful content, restrict sensitive information and enforce acceptable responses.

Guardrails introduce an additional layer of protection between users and foundation models, helping organizations maintain consistency and reduce the likelihood of inappropriate outputs. While guardrails cannot eliminate every risk associated with generative AI, they represent an important step towards secure and responsible AI adoption.

Building An Effective Shadow AI Governance Strategy
Technology alone cannot solve the Shadow AI problem. Organizations must establish governance frameworks that balance innovation with security.

Providing employees with approved AI platforms is more effective than attempting to prohibit AI usage entirely. Services such as Amazon Bedrock and Amazon Q Developer allow organizations to offer secure alternatives while maintaining visibility and control.

Identity and access management also play a critical role. AI systems should follow the principle of least privilege, receiving only permissions required to perform their intended tasks. Temporary credentials and IAM roles should be preferred over long term access keys.

Employee awareness is equally important. Many Shadow AI incidents results from curiosity rather than malicious intent. Security training should emphasize responsible AI usage, prompt hygiene, and data classification practices.

Continuous monitoring remains essential. CloudTrail, GuardDuty, Security Hub, and Macie should work together to provide visibility and early detection capabilities. Ultimately successful governance depends on people, processes, and technology working together.

Looking Ahead
The rise of Shadow AI strongly resembles the emergence of Shadow IT years ago. Initially organizations attempted to eliminate unsanctioned technologies through restrictive policies. Over the time, however it became clear that innovation moves faster than prohibition.

The same lesson applies today. Employees will continue to adopt AI technologies because the productivity benefits are undeniable. Developers will use coding assistants, analysts will leverage generative AI, and organizations will increasingly rely on intelligent automations. The question is no longer whether AI will become part of enterprise operations. It already has. The more important question is whether organizations can govern this transformation before visibility is lost.

Conclusion
Shadow AI is not merely an AI problem. It is a visibility problem. It is a data protection problem. It is a governance problem. And increasingly, it is an identity problem.

AWS provides powerful capabilities including Amazon Macie, AWS CloudTrail, Amazon GuardDuty, AWS Security Hub, AWS Security Config and Amazon Bedrock Guardrails that can help organization establish visibility and strengthen governance.

However technology alone is not enough. Organizations that succeed in the age of AI will be those that combine security controls with clear policies , continuous monitoring, and employee awareness. Because in 2026, the greatest risks may not come from AI systems you intentionally deploy but from the ones operating quietly in the shadows.

Exploring the Ethereum Blockchain: A Deep Dive into Decentralized Innovation

adi271001 — Sat, 01 Mar 2025 16:53:23 +0000

Introduction

Ethereum, the pioneer of smart contract-enabled blockchain technology, has revolutionized the digital landscape. In this blog, we explore the Ethereum ecosystem, its functionalities, and its impact. This deep dive will provide insights into its architecture, transaction mechanisms, security considerations, and dApp development.

What is HackQuest?

@HackQuest_ is a leading platform that empowers blockchain enthusiasts through interactive learning tracks and co-learning camps. It provides hands-on experience with Ethereum and other blockchain technologies, making it an excellent resource for anyone looking to dive deep into Web3 development.

Understanding Ethereum: The Backbone of Web3

Ethereum extends beyond cryptocurrency; it enables decentralized applications (dApps) using smart contracts, eliminating intermediaries and ensuring transparency.

Key Features of Ethereum

Feature	Description
Smart Contracts	Self-executing contracts written in Solidity that automate transactions.
Ethereum Virtual Machine (EVM)	A computational layer enabling smart contract execution across Ethereum nodes.
Decentralized Applications (dApps)	Applications powered by Ethereum's decentralized infrastructure.
Ethereum Gas Fees	A small computational fee required for transactions and contract execution.
Ethereum 2.0 Upgrade	Transition to Proof of Stake (PoS) for improved efficiency.

How Ethereum Transactions Work

Ethereum transactions follow a structured process, from initiation to confirmation on the blockchain.

Ethereum Transaction Lifecycle:

Transaction Initiation: The user submits a transaction, such as sending ETH or interacting with a smart contract.
Validation: The transaction is verified and checked for sufficient gas fees.
Mining/Proof of Stake: The transaction is added to a block.
Smart Contract Execution: If applicable, the smart contract logic executes.
Final Confirmation: The transaction is permanently recorded on the Ethereum blockchain.

Ethereum Security: Protecting the Blockchain

Security is paramount in Ethereum, ensuring that smart contracts and user assets remain protected from vulnerabilities and attacks.

Common Ethereum Security Threats

Security Risk	Description
Reentrancy Attacks	An attacker repeatedly calls a contract before previous executions complete, draining funds.
Front-Running	Malicious actors exploit transaction ordering to gain unfair advantages.
Phishing Scams	Fake websites trick users into revealing private keys or mnemonic phrases.
51% Attacks	If a single entity controls over 50% of Ethereum’s network, they could manipulate transactions.

Ethereum Smart Contracts: The Heart of Decentralization

Smart contracts automate transactions by executing predefined conditions, ensuring security and trustless execution.

Example Solidity Smart Contract:

pragma solidity ^0.8.0;
contract SimpleContract {
    string public message;
    constructor(string memory _message) {
        message = _message;
    }
    function updateMessage(string memory _newMessage) public {
        message = _newMessage;
    }
}

Developing dApps on Ethereum

Ethereum allows developers to create dApps that leverage blockchain transparency and security.

Steps to Develop an Ethereum dApp:

Set Up Development Environment: Install Node.js, Truffle, and Ganache.
Write Smart Contracts: Use Solidity to define contract logic.
Deploy on a Testnet: Deploy contracts to Ropsten, Rinkeby, or Goerli.
Integrate Frontend: Use Web3.js or Ethers.js to interact with the blockchain.
Launch on Mainnet: Deploy to Ethereum Mainnet after testing.

Ethereum’s Role in Web3

Ethereum powers Web3 applications by providing a decentralized framework for data and transactions, moving away from traditional web models reliant on central entities.

Web3 vs. Web2 Comparison Table:

Feature	Web2	Web3
Control	Centralized	Decentralized
Data Ownership	Companies	Users
Security	Prone to Breaches	Blockchain-Backed
Transparency	Limited	Complete
Intermediaries	Required	Eliminated
Monetization	Ad-Based	Tokenized

Layer 2 Solutions: Linea and Polygon

Linea: Scaling Ethereum with zkEVM

Linea is a Layer 2 scaling solution that enhances Ethereum’s performance using zkEVM technology. It allows developers to deploy smart contracts seamlessly while benefiting from Ethereum’s security.

Polygon: A Multi-Chain Ethereum Scaling Framework

Polygon provides a suite of scaling solutions, including sidechains, Plasma, and zk-rollups, to reduce transaction fees and enhance Ethereum’s usability.

Solana vs. Ethereum: A Comparison

Solana and Ethereum are two leading blockchain platforms, each with unique strengths.

Feature	Ethereum	Solana
Consensus	Proof of Stake (PoS)	Proof of History (PoH)
Transaction Speed	15-30 TPS	65,000+ TPS
Scalability	Layer 2 Solutions	High Scalability
Smart Contracts	Solidity-based	Rust-based
Decentralization	High	Moderate

Learning Ethereum on HackQuest

HackQuest provides structured learning paths to help users understand Ethereum and Web3 technologies. Some of the key modules include:

Phase 1: Web3 Basics

Web3 Overview
Introduction to Blockchain Technology
Wallet Principles and Security
Blockchain Application Scenarios

Phase 2: Web3 Security

Common Wallet Risks
Security of Private Keys and Mnemonic Phrases
Digital Signature Risks
Fraud Tactics & Prevention Measures

How to Get Started with HackQuest

Step-by-Step Guide:

Sign Up: Create an account on HackQuest.
Choose a Learning Track: Pick the Ethereum track to start learning.
Complete Tasks: Follow interactive lessons and quizzes.
Earn Rewards: Gain XP points and blockchain certifications.
Contribute & Network: Engage in discussions and co-learning camps.

Conclusion

Ethereum is shaping the decentralized future, from finance to gaming, by empowering users with control and transparency. Understanding its fundamentals opens doors to building and participating in the blockchain revolution. Platforms like HackQuest make this learning journey seamless, providing real-world blockchain experience and community support.

Author: Aditya D\
GitHub: https://www.github.com/adi271001\
LinkedIn: https://www.linkedin.com/in/aditya-d-23453a179/\
Topmate: https://topmate.io/aditya_d/\
Twitter: https://x.com/ADITYAD29257528

Developing An Internal Developer Platform

adi271001 — Thu, 25 Jul 2024 11:48:56 +0000

In this blog we will be discussing about Internal Developer Platform (IDP), what it is , how to set one up as a lead DevOps Engineer.Present tech landscape implementing an IDP helps to streamline workflows and boost productivity.

What is an Internal Developer Platforming

It is a platform or an application which is built by platform engineecloring team for building golden paths and to facilitate developer self service. It consists of various tools and technologies integrated together so that it lowers cognitive load on developers without abstraction of context and underlying technologies.

Why is there a need to build IDP

It Centralizes and simplifies application deployment and management.

Simplify Deployment:

Helps engineers to deploy applications with minimal manual intervention.

Increase Reliability:

This ensures that deployments are consistent and error free.

Enhance Productivity:'

Reduces time and efforts invested for managing infrastructure

Tools and Technologies Used

Kubernetes

This is a container orchestration tool which is used for automating deployments , scaling and management of applications which are containerized.

Documentation: https://kubernetes.io/docs/home/
Youtube: https://youtu.be/X48VuDVv0do?si=9aPNky0DDPyATiFM
Interview Questions: https://kuberty.io/blog/top-kubernetes-interview-questions/

Cyclops

It is a user friendly UI and streaming tool for managing kuberenetes clusters.

Documentation: https://cyclops-ui.com/docs/about/
Youtube: https://www.youtube.com/live/QQAqHGEYVRM?si=GEgZ3bDyhsCh_cHV
Github Repository: https://github.com/cyclops-ui/cyclops

Docker

It is a tool which is used for easy developing , shipping and running applications inside a container

Documentation: https://docs.docker.com/
Youtube: https://youtu.be/3c-iBn73dDE?si=FI7ZKDi3Kij1MYMo
Interview Questions: https://hackr.io/blog/docker-interview-questions

Helm

It is a package manager for kubernetes which helps in simplification of deployments and services

Documentation: https://helm.sh/docs/
Youtube: https://youtu.be/5_J7RWLLVeQ?si=8DPo9rD5cYf-moJd
Interview Questions: https://www.devopsschool.com/blog/top-50-helm-interview-questions-and-answers/

Prometheus

It is used for monitoring and alert management

Documentation: https://prometheus.io/docs/introduction/overview/
Youtube: https://youtu.be/h4Sl21AKiDg?si=2Heu9OAkG_FkZSNJ
Interview Questions: https://www.devopsschool.com/blog/top-50-prometheus-interview-questions-and-answers/

Graphana

It is a visualization tools for visualizing data

Documentation: https://grafana.com/docs/
Youtube: https://youtu.be/CjABEnRg9NI?si=QrZ3aBVUy_B9LOKH
Interview Questions: https://www.devopsschool.com/blog/top-50-grafana-interview-questions-and-answers/

PostgreSQL and MongoDB

These are popular relative and NoSQL databases respectively and is used for storing application data.

Documentation-PostgreSQL: https://www.postgresql.org/docs/
Youtube-PostgreSQL: https://youtu.be/qw--VYLpxG4?si=rory5ndP3nqGzKjZ
Interview Questions-PostgreSQL: https://www.datacamp.com/blog/top-postgresql-interview-questions-for-all-levels
Documentation-MongoDB: https://www.mongodb.com/docs/
Youtube-MongoDB: https://youtube.com/playlist?list=PL4cUxeGkcC9h77dJ-QJlwGlZlTd4ecZOA&si=N49ED6H2sztBQrFu
Interview Questions: https://hackr.io/blog/mongodb-interview-questions

Istio

It is a service mesh for advanced traffic management , security , observability for microservice applications

Documentation: https://istio.io/latest/docs/
Youtube: https://youtube.com/playlist?list=PLI4xy7phW54nbfjf7ZMnlEx1O5cHneigh&si=bs3RdBT6Tm6SF9Fc
Interview Questions: https://www.devopsschool.com/blog/top-50-istio-interview-questions-and-answers/

Jenkins

It is an open source automation server tool which is used for Continuous Integration (CI) and Continuous Delivery (CD), which automates building, testing and deploying applications via pipelines.

Documentation: https://www.jenkins.io/doc/
Youtube: https://youtube.com/playlist?list=PLy7NrYWoggjw_LIiDK1LXdNN82uYuuuiC&si=yjaMJdICFYJlCFZY
Interview Questions: https://hackr.io/blog/jenkins-interview-questions

Argo CD

it is a gitops CD tool for kubernetes , it is declarative and ensures the desires state of deployed application is matched as defined in git repository

Documentation: https://argo-cd.readthedocs.io/en/stable/
Youtube: https://youtu.be/MeU5_k9ssrs?si=PUzJxpQobUiVcqvm
Interview Questions: https://www.lwplab.com/forum/questions-answers/interview-questions-answers-on-argo-cd

Implementation

Step 1: Setup Docker

sudo apt-get update
sudo apt-get install apt-transport-https ca-certificates curl software-properties-common
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
sudo add-apt-repository "deb [arch=amd64] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable"
sudo apt-get update
sudo apt-get install docker-ce

Step 2: Start Docker

sudo systemctl start docker
sudo systemctl enable docker

Step 3: Set up kubernetes

curl -s https://packages.cloud.google.com/apt/doc/apt-key.gpg | sudo apt-key add -
echo "deb https://apt.kubernetes.io/ kubernetes-xenial main" | sudo tee /etc/apt/sources.list.d/kubernetes.list
sudo apt-get update
sudo apt-get install -y kubectl

Step 4: Set Up Minikube

curl -LO https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64
chmod +x minikube-linux-amd64

Step 5: Start Minikube

minikube start

Step 6: Install Helm

curl https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3 | bash

Step 7: Install Cyclops-UI

kubectl apply -f https://raw.githubusercontent.com/cyclops-ui/cyclops/v0.8.2/install/cyclops-install.yaml && kubectl apply -f https://raw.githubusercontent.com/cyclops-ui/cyclops/v0.8.2/install/demo-templates.yaml

Step 8: Expose cyclops and access it

kubectl port forward svc/cyclops-ui 3000:3000 -n cyclops

cyclops is now accesssible in browser by using this url: http://localhost:300

Step 9: Install and Configure Databases

PostgreSQL

helm repo add bitnami https://charts.bitnami.com/bitnami
helm install postgresql bitnami/postgresql

MongoDB

helm install mongodb bitnami/mongodb

Step 10: Install Monitoring and Logging Tools

Prometheus

helm install prometheus bitnami/prometheus

Grafana

helm install grafana bitnami/graphana

configure grafana to add prometheus as data source, below is example yaml file named as grafana-datasource.yaml

Step 11: Install and implement Service Mesh with Istio

curl -L https://istio.io/downloadIstio | sh -
cd istio-*
export PATH=$PWD/bin:$PATH
istioctl install --set-profile=demo -y
kubectl label namespace default istio-injection=enabled

deploy a sample app using istio , below is example yaml file named as sample-app.yaml used for deployment

deploy using kubectl apply -f sample-app.yaml

Step 12: Install ArgoCD

kubectl create namespace argocd
kubectl apply -n argocd -f https://raw.githubusercontent.com/argoproj/argocd/stable/manifests/install.yaml

Expose the argocd to get access

kubectl port-forward svc/argocd-server -n argocd 8080:443

kubectl -n argocd get secret argocd-initial-admin-secret -o jsonpath="{.data.password}" | base64 -d

Step 13: Install Jenkins

kubectl create namespace jenkins
helm repo add jenkins https://charts.jenkins.io
helm repo update
helm install jenkins jenkins/jenkins --namespace jenkins

Expose jenkins to get access

kubectl port-forward svc/jenkins -n jenkins 8080:8080

kubectl exec --namespace jenkins -it svc/jenkins -c jenkins -- /bin/cat /run/secrets/additional/chart-admin-password && echo

Step 14: Set up Jenkins Pipeline;

Access jenkins using the url http://localhost:8080, login using the admin password and username as admin, below is the sample image of jenkins console

now create a sample app in your local directory, it can be in any programming language , below is the sample app structure in node js format

next create a Dockerfile to run docker container , sample Dockerfile is shown below

now create a Jenkinsfile which contains the steps for jenkins pipeline , a sample pipeline is shown below

at last create a deployment.yaml file for kubernetes and other services to run , below is sample file for deployment.yaml

Usecase

Let's consider an E-commerce company which requires frequent updates and deployments and engineers are struggling with their deployments which is manual , inconsistent , lack of visibility. The answer to tackle these issues is building and IDP. Lets see how each tool helps in solving these problems.

Cyclops-Ui

Pre Deployment: Cyclops Ui can be accessed to set parameters
like environment settings and resource allocation.
Deployment: the pipeline will be triggered by Cyclops-UI
which leverages tools like kubernetes , docker and helm and
ensures consistent and reliable manner of deployment.

Jenkins

Automated Builds: The source code repository in github is
monitored by jenkins and an immediate trigger for new build
happens when any change is detected in the repository.
Automated Tests: Once the changes are applied jenkins also
tests the changes to ensure no bugs or regressions.
Deployment: After tests are successful the application is
deployed on staging environment by jenkins and after validations
are done it is deployed to production environment.

Argocd

Continuous Delivery: Detects changes in repository and synchronizes the kubernetes cluster to the desires state mentioned in manifest file.

Prometheus

This can be used to collect metrics from application and kubernetes cluster which provides insights into performance and cluster health.

Graphana

This is used for visualizng the metric data taken from prometheus
and create dashboards and monitor the application in real time

PostgreSQL and MongoDB

These are the primary databases of the application which uses transactional and non relational data respectively, it is managed by helm charts for consistency and scalability.

Istio

Traffic Management: Istio provides features for managing
traffic between microservices , for loadbalancing and for
routing as well.
Security: Security policies are enforced by istio to ensure
secure communication between services.

Benefits

Efficient Deployment: Since deployment process is automated
manual errors are reduced and consistency is ensured.
Enhanced Monitoring: With realtime dashboards ,visualization
performance and issues are being monitored effortlessly.
Scalable Infrastructure: With tools like kubernetes , docker
, helm applications can be scaled and managed easily also
effective resource allocation is done without any difficulty.
Streamlined CI/CD: With jenkins building , testing and
deployment is automated and consistent as and when changes in
source repository are detected.
GitOps Workflow: With tool like ArgoCD deployed applications
will always seam lessly match desired states mentioned in
manifest file in git repository.

Final Thoughts

Building an IDP using cyclops and kubernetes includes setting up a variety of tools whcih ensures smooth and efficient deployment and management process. By using tools like Docker , Helm , ArgoCD, prometheus , graphana, PostgreSQL, MongoDB, Istio , Jenkins we have built a comprehensible and reliable platform which makes deployments and management of projects very easy.

By using a range of DevOps and CI/CD tools we have built a powerful environment which accelerates development and deployments. We have also seen an example usecase of E-Commerce website which shows how efficiently deployments can be managed and how it enhances both productivity and reliability