DEV Community: Anthony Max

Most AI reviewers know how to leave comments. A completely different question is: can a tool turn your engineering standards into mandatory rules that get checked on every pull request and applied even before the code reaches the PR?

Three platforms often end up on the same shortlist: Qodo, CodeRabbit and Cubic. At first glance all three "review code". But architecturally they solve different problems, and only one of them was originally built as a code governance platform, not as a "smart commenter".

Want to know which one? In this article, I'll try to tell you about it!

💬 To put it bluntly

To find out the answer to this question, I have prepared a small table with the differences.

Platform	Main focus	Approach to rules	Blocks merge?
Qodo	Code review + Code Governance	Centralized Review Standards and Rules: explicit, enforceable conventions	No; findings carry a severity and an action level, merge gate via branch protection or CI
CodeRabbit	Agentic Change Management	Code Guidelines + Pre-Merge Checks (default: warning)	Only with Request Changes Workflow enabled + mode `error`
Cubic	AI code review on GitHub	Custom agents (up to 5 per repo), natural language rules	No; auto-approval for "clean" PRs, but no enforcement of standards

From this you can conclude that if you need a standards management system, not just AI comments, Qodo covers this scenario natively. CodeRabbit and Cubic, on the other hand, are strong in the review workflow, but their "rules" more often work as recommendations or optional quality gates.

But what does this actually mean? I'll try to tell you a bit about it.

🔎 What's the difference between Suggest and Enforce?

Honestly, an AI reviewer can work in two fundamentally different modes. The first of them Suggest is a recommendation: the tool writes, for example, "here it's better to add a retry", and the developer decides for themselves whether to apply the change. And the second one, Enforce is already a formalized rule: the system checks the PR against the standard, records a violation as a finding, allows tracking it at the level of the whole organization, and flags it with a severity and an action level, reporting it if the PR merges anyway — the merge gate itself comes from branch protection or CI, not Qodo.

The difference is especially important for compliance and security policies, monorepos with dozens of teams, and distributed systems, where a change in one repository can cause errors when changes are introduced in code in another. At scale, this approach lets you not just give developers advice, but consistently apply engineering standards across the whole organization.

Suggest:

Enforce:

Qodo would have flagged this violation with a severity and an action level before anyone merged it.

If a reviewer accidentally accepts a PR, it may take some time to fix the code. With enforced code, the chances of this happening are lower.

Now let's look at the rules and how each tool implements them.

✅ Qodo: governance as part of the platform, not an add-on

Qodo goes beyond ordinary AI code review: it's a Code Governance platform that combines review, standards, cross-repo analysis and agentic workflows in one product.

Review Standards: rules that get checked, not "wishes"

In Qodo rules — explicit, enforceable conventions that are checked during code review. Each rule captures one engineering standard: how to write code, how to structure changes, what counts as a violation.

Rules are added in three ways:

Method	How it works
Manual	Rule generation from a natural language description in the portal
File-based	Auto-import from `AGENTS.md`, `CLAUDE.md` and other supported files on push
Rule Miner	Automatic pattern discovery from your organization's PR history

In cases where Review Standards are not applicable, Qodo applies its established best practice checks and configured compliance files.

Rule Miner: tribal knowledge → enforceable standards

Rule Miner analyzes the history of pull request discussions and finds recurring review patterns: comments that developers accepted and actually fixed in the code.

What's taken into account:

Signal	How it works
Reviewer code ownership	Comment weight is proportional to the reviewer's ownership
Recurring comments	Repeated feedback → rule candidate
Pattern clustering	Area-specific rules with path scope
Rejected suggestions	Dismissed findings reduce the rule's signal over time

Rule Miner works as a learning system: the first run happens after a PR is opened (up to 10 rules), then every 2 weeks. None of the competitors in this comparison offers an equivalent to "mining rules from your actual review history".

Multi-agent review + PR history indexing

Qodo uses multi-agent expert review: separate agents for logic/bugs, standards compliance, architecture and risk assessment. A judge agent consolidates findings and filters by confidence.

Qodo 2.2 brought PR history based Relevance; PR history indexing came in Qodo 2.7. The platform understands how your team actually reviews code:

Which findings were accepted
Which were ignored
What led to changes in the code

Relevance prioritizes suggestions that the team historically acts on. This reduces noise and increases signal enforcement, not just the number of comments.

Shift-left: rules before the PR, not after

Qodo Agentic Toolbox: Get Rules loads applicable rules before the code is written in Cursor, Claude Code, Codex or via CLI/MCP. Here's example output from the Get Rules tool for a task like adding retries to a payments client:

task: add retries to payments client

Global  → Error handling: no silent catches
Payments → Idempotency keys on every write
Repo    → Structured logging conventions

Loaded before the first line is written

This is the key difference of the governance approach: standards work as guardrails during authoring, not just as post-hoc comments in the PR.

🔧 The Agentic Toolbox: agent-to-agent review, not just IDE hints

Starting September 9, Qodo is extending this shift-left idea into something bigger than a single "get rules" call: the Agentic Toolbox. It gives Claude Code, Codex, Kiro, Cursor, and anything speaking MCP direct access to Qodo's review and governance layer, without leaving the coding session.

The core idea is agent-to-agent review: the coding agent writes, an independent Qodo-powered reviewer checks the work as it goes, and only what the two can't resolve between them reaches a human. It doesn't replace PR review or human review, it sits earlier in the loop and reduces what ends up needing either. Five tools carry this:

Codebase Wisdom answers questions like "which repos break if I change this," using Qodo's relationship graph, PR history, specs, and live Git state, no local clone or manual grepping needed.
Get Rules loads the workspace's rules into the session before the agent writes, so standards land in the first draft instead of the fifth review comment.
Review runs Qodo's full review engine on local committed and uncommitted work, sharpened by the agent's session context.
Review Resolver pulls a PR's structured findings straight from Qodo's review database and resolves them, once or in a watch loop.
Manage Standards lets admins create, update, and re-scope the team's rules directly from an agent session.

It installs from the Claude Code, Codex, or Kiro marketplace, from the terminal, or as an MCP server for any MCP-compatible agent; the CLI is just how you install and run it, the toolbox itself is the five tools, not a CLI product on its own.

Here's the part that ties back to everything above: rules stop being something you only enforce at review time. Admins can create, update, and re-scope rules directly from inside an agent session, conversationally, instead of editing a config file. Paired with Rule Miner auto-discovering and versioning patterns from real review history, that pushes Qodo's governance model further from "a static rules file read once per review" toward something that's actually alive across the whole loop: before the code is written, before the PR opens, and after, if a rule turns out to be noisy.

Cross-repository governance

Cross-repository code review catches breaking changes between repositories: shared library, API contract, schema, pipeline artifacts. Findings are tagged Cross-repo and are visible in the org-wide Findings dashboard.

For distributed systems this is enforcement at the system level, not a single diff.

Risk triage: blast radius

Blast radius classifies a PR as Small / Medium / Large risk and publishes a native label in GitHub/GitLab/Azure DevOps, which helps with triage before the deep review.

🐇 CodeRabbit: strong review, governance through optional gates

CodeRabbit positions itself as Agentic Change Management: AI reviews, Triage, Change Stack, Security, Plan. It's a powerful review workflow, but the rules model here is different.

Code Guidelines = review criteria, not an org-wide rules engine

CodeRabbit automatically picks up AGENTS.md, CLAUDE.md, .cursorrules and applies them as review criteria. This is convenient, but:

Guidelines are scoped by directory/monorepo paths
No centralized portal for the org-wide rule lifecycle
No Rule Miner / rule analytics / mined-from-history activation flow
Trash in the UI disables a guideline but doesn't create an enforceable org standard

Pre-Merge Checks: closest to enforcement

CodeRabbit Pre-Merge Checks remain the only mechanism that can block a merge:

Built-in Check	What it checks
Docstring Coverage	Docstring coverage in the diff (threshold, default 80%)
Pull Request Title	Whether the title matches the changes
Pull Request Description	Compliance with the template
Issue Assessment	Whether the PR addresses the linked issue without going out of scope

Custom Checks (Team plan+): natural language validation (up to 1000 chars).

But enforcement modes:

Mode	Behavior
`off`	Disabled
`warning`	Default: shows a warning, doesn't block
`error`	Blocks the merge only with Request Changes Workflow

Request Changes Workflow disabled by default. Override via @coderabbitai ignore pre-merge checks or a checkbox in the walkthrough. This works per-PR, not as an org policy.

Bottom line on CodeRabbit: enforcement is possible, but it's an opt-in layer on top of suggestion-first review. By default you get warnings, not a full-fledged standards platform.

🎲 Cubic: custom agents for GitHub review

Cubic positions itself as an AI code reviewer and ranks highly on the Code Review Bench for complex codebases. The focus here is on the quality of review comments, not org governance.

Custom agents = suggestion rules with limits

Custom agents are defined as natural language rules, for example "Flag N+1 queries in loops":

Limit	Value
Active agents per repo	Max 5
Instruction limit	10,000 chars (text + linked files)
Git platforms	GitHub only (per the FAQ on the intro page)
Org-wide portal	Rules library, but without Rule Miner / cross-repo governance / rule analytics

A custom agent flags an issue in a comment with attribution, but it's still a suggestion, not enforcement. Cubic itself honestly warns about this: «Consider AI feedback as helpful guidance rather than definitive judgments.»

Auto-approval speeds up merging, but it's not enforcement

Cubic auto-approval speeds up merging for clean, low-risk PRs. It's more the opposite of strict enforcement than an equivalent of it. Shadow → Live rollout and never-auto-approve paths: this is risk reduction, not standards compliance.

Memory & learning

Cubic learns from feedback (reactions, corrections), meaning it personalizes suggestions. Qodo learns through PR history indexing + Rule Miner, meaning it formalizes patterns into enforceable rules. The "learning" models here are different.

📊 Comparison tables

1. Governance & Rules

Capability	Qodo	CodeRabbit	Cubic
Centralized rules portal	✅ Yes – Review Standards	⚠️ Partial – Org settings + YAML	⚠️ Partial – Rules library
Explicit enforceable Rules	✅ Yes	⚠️ Partial – Custom Pre-Merge Checks only	⚠️ Partial – Custom agents (comments)
Auto-import from AGENTS.md / CLAUDE.md	✅ Yes – File-based rules	✅ Yes – Code Guidelines	⚠️ Partial – Linked files in agents
Rules from PR history (Rule Miner)	✅ Yes	❌ No	❌ No
Rule analytics	✅ Yes	❌ No	❌ No
Org-wide findings dashboard	✅ Yes	⚠️ Partial – Triage / reports	⚠️ Partial – Analytics
Cross-repo breaking change detection	✅ Yes – Code Governance	⚠️ Partial – Multi-Repo Analysis (KB)	⚠️ Partial – Cross-repo reviews
Shift-left rules in IDE/agent	✅ Yes – Get Rules (Agentic Toolbox)	⚠️ Partial – IDE extension (review)	⚠️ Partial – IDE + CLI review

2. Enforcement vs Suggestion

Behavior	Qodo	CodeRabbit	Cubic
Default mode	Rule-based findings	Suggestions + warnings	Suggestions
Block merge on violation	⚠️ Partial – Findings carry severity and action level; merge gate via branch protection or CI	⚠️ Partial – Only with Pre-Merge mode `error` + Request Changes Workflow	❌ No
Default blocks merge?	Findings require action	❌ No – warning is the default	❌ No
Per-PR override	Team process	✅ Yes – Ignore checkbox / command	—
Auto-approve clean PRs	—	—	✅ Yes

3. Review Intelligence

Capability	Qodo	CodeRabbit	Cubic
Multi-agent review	✅ Yes – Specialized agents + judge	✅ Yes – Multiple AI models	✅ Yes – Multi-model
PR history learning	✅ Yes – Relevance	⚠️ Partial – Learnings (chat)	⚠️ Partial – Memory
Risk classification	✅ Yes – Blast radius labels	⚠️ Partial – Triage prioritization	—
50+ linter integrations	—	✅ Yes – Tools catalog	—
Security scanning	⚠️ Partial – Via rules/compliance	✅ Yes – CodeRabbit Security	⚠️ Partial – Codebase scans

4. Platform & Deployment

	Qodo	CodeRabbit	Cubic
GitHub	✅ Yes	✅ Yes	✅ Yes
GitLab	✅ Yes	✅ Yes	❌ No
Bitbucket	✅ Yes	✅ Yes	❌ No
Azure DevOps	✅ Yes	✅ Yes	❌ No
On-prem / self-hosted	✅ Yes	⚠️ Partial – Cloud-focused	❌ No
Config file	`.pr_agent.toml`	`.coderabbit.yaml`	`cubic.yaml`

👀 When to choose what

Choose Qodo if:

You need a unified standards system for the whole organization, not a pile of comments
You want to automatically extract rules from real review history (Rule Miner)
You work with monorepo / multi-repo and need cross-repo enforcement
Shift-left matters to you: rules in the IDE before the PR (Agentic Toolbox)
You need on-prem or enterprise governance with SSO/RBAC (Account Management)

CodeRabbit is a good fit if:

Priority — review UX (Triage, Change Stack, walkthroughs, autofix)
You need 50+ linters in one review pipeline
Enforcement is only needed in specific spots through Pre-Merge Checks, and the team is ready to manually enable error + Request Changes

Cubic is a good fit if:

The team works only on GitHub
You need a strong AI reviewer for complex codebases
5 custom agents per repo and a suggestion-based workflow are enough
You want auto-approval for low-risk PRs

🖋️ Conclusion

CodeRabbit and Cubic do AI review at a high level: they find bugs, improve diff quality and speed up the feedback loop. But their "rules" remain recommendations in most scenarios: optional gates for CodeRabbit, bounded custom agents for Cubic.

Qodo was originally designed as a Code Governance platform: Review Standards, Rule Miner, rule analytics, cross-repo review and shift-left through the Agentic Toolbox. It's not "just another bot that comments on PRs", it's infrastructure for enforcing engineering standards at scale.

If your question sounds like «who just gives advice?», all three platforms can do that.

But if the question is «who turns our standards into enforceable rules and tracks them at the organization level?», there's one answer: Qodo.

🔗 Links:

Qodo Site: https://qodo.ai
Qodo Docs: https://docs.qodo.ai
Qodo Plugin: https://marketplace.visualstudio.com/items?itemName=Codium.codium&ssr=false#overview
Code Review Academy: https://www.qodo.ai/academy
Code review tools: https://www.qodo.ai/academy/ai-code-review-tools-comparison-and-benchmarks

Thanks for reading this article! ❤️

I'd love to hear your thoughts on this plugin in the comments!

I can't believe it, but I've been actively writing articles for two years now. A huge thank you to dev.to for the opportunity to create content!

Anthony Max — Mon, 21 Sep 2026 13:47:41 +0000

[Boost]

Anthony Max — Sun, 20 Sep 2026 08:11:48 +0000

Anthony Max

Sep 19

I created a catalog with embeds for your projects 💎

#ai #webdev #programming #opensource

1 min read

I created a catalog with embeds for your projects 💎

Anthony Max — Sat, 19 Sep 2026 22:00:44 +0000

Hello everyone!

Today, I’d like to introduce a new version of EmbedCatalog. It’s a personal project that I created in just a few weeks:

🌌 Embeds

Here you can create your own embeds and use them to decorate your applications or Markdown files.

👀 How the app works

I’ve added registration and login functionality, as well as an embed editor. You can watch the video for more details here:

// Detect dark theme var iframe = document.getElementById('tweet-2101420075840647541-162'); if (document.body.className.includes('dark-theme')) { iframe.src = "https://platform.twitter.com/embed/Tweet.html?id=2101420075840647541&theme=dark" }

🌱 This project is open source

I would be very grateful if you starred the repository:

embedcatalog / embedcatalog

Decorate your project with embed. Catalog of services, open source libraries and other tools.

EmbedCatalog

This is a project catalog where you can add yours and get interesting and practical embeds.

Briefly about the idea

While developing open source projects, one day I came up with the idea of making a website for practical pictures for projects, since I wanted to somehow highlight my project rather than adding default ones. I don't want to copy others, so I made a catalog where each project will have a relatively unique embed.

Embeds

The platform currently has the following list of embeds:

Embed	Light	Dark	Plan
License			Free
Added to			Free
Organization			Premium

What projects are suitable?

Open source
Service
Web app
Developer tool
Package
Other technical

Commercial ones are also taken into work.

About premium

Essentially, we are not focused on creating an audience on the platform, although this is also important. The idea is to work on a project periodically, like an ant working…

View on GitHub

Thank you very much for your attention! It will be interesting to hear your opinion on it ❤️.

The site itself is here:

https://embedcatalog.com

[Boost]

Anthony Max — Sat, 19 Sep 2026 21:47:04 +0000

Anthony Max

Sep 16

Top AI Security Tools for Enterprises (2026) 💎

#ai #webdev #programming #opensource

9 min read

[Boost]

Anthony Max — Thu, 17 Sep 2026 20:04:07 +0000

Anthony Max

Sep 16

Top AI Security Tools for Enterprises (2026) 💎

#ai #webdev #programming #opensource

9 min read

Best

Anthony Max — Wed, 16 Sep 2026 19:17:52 +0000

Anthony Max

Sep 16

Top AI Security Tools for Enterprises (2026) 💎

#ai #webdev #programming #opensource

9 min read

Top AI Security Tools for Enterprises (2026) 💎

Anthony Max — Wed, 16 Sep 2026 18:53:32 +0000

Enterprise AI adoption has outpaced security architecture. Employees are using desktop chat apps and terminal coding agents, often without IT ever setting up a gateway or policy layer. Sensitive prompts leave laptops with basically no oversight. MCP servers connect to files and APIs with very little visibility into what's actually going on. Shadow AI isn't some niche risk anymore, it's the default at most organizations.

In 2026, the leading security teams are converging on more or less the same model: discover what AI is actually in use, enforce policy at the point of interaction, and apply guardrails before data ever reaches a model. The five platforms below are the strongest enterprise options across that stack, and Bifrost together with Bifrost Edge stands out as the pick for endpoint governance and policy enforcement.

💎 1. Bifrost + Bifrost Edge: Policy enforcement from gateway to endpoint

Best for: Organizations that need a unified AI policy layer across desktop apps, browser AI, coding tools, and MCP, without asking users to reconfigure every application.

Most AI gateways only protect the traffic developers explicitly route through them. Bifrost Edge closes that gap. It runs natively on macOS, Windows, and Linux and routes all AI traffic (chat apps, browser AI, coding agents, MCP servers) through your existing Bifrost gateway. Virtual keys, budgets, audit logs, and guardrails that already apply to gateway traffic now apply to the AI people are actually using on their machines.

Core capabilities

Component	Function
LLM Gateway	Routes and governs all LLM traffic across models and providers, with ultra-low latency
MCP Gateway	Governs MCP server connections and tool calls, enforcing allow/deny policy centrally
Agents Gateway	Extends the same routing and governance to coding agents and other autonomous AI workflows
Bifrost Edge	Brings gateway-level policy down to the desktop, browser, and terminal, with no reconfiguration needed
Enterprise Guardrails	Validates inputs, outputs, and tool executions in real time against configurable rules and profiles

Endpoint security

According to Bifrost Edge documentation, Edge is meant to be invisible after a one-time browser sign-in via SSO. Users just keep using Claude Desktop, ChatGPT, Cursor, Codex, and whatever else they already had, while Edge intercepts and routes requests through Bifrost in the background. No proxy settings to configure, no base URL changes, no API keys pasted into random apps.

Edge covers three critical endpoint surfaces:

Surface	Examples	What Edge does
Desktop apps	Claude Desktop, ChatGPT app, Cursor, Codex	Routes and governs AI traffic from native clients
Browser AI	ChatGPT web, Claude web	Governs conversations on browser-based AI surfaces
Coding agents	Claude Code, Codex CLI, OpenCode	Routes terminal and IDE agent traffic through policy

This machine-level routing means governance follows the person instead of sitting around waiting for someone to opt in. As Bifrost Edge's overview puts it, the same virtual keys, budgets, audit logs, and guardrails that already existed now cover the AI people are actually using day to day, not just whatever traffic happened to be configured on purpose.

App and MCP governance on the device

Beyond routing, Edge also gives administrators fleet-wide control:

App governance: admins decide centrally which AI applications are allowed. Approved apps run normally through Bifrost, anything not on the list gets blocked before data ever leaves the machine, and new apps trigger an approval workflow in the admin console.
MCP governance: Edge inventories the MCP servers configured inside supported apps (Claude Code, Claude Desktop, Cursor, Codex, Gemini CLI, OpenCode) and enforces allow/deny decisions right on the device, not just as advisory policy sitting in a dashboard somewhere.

MCP is a growing blind spot for a lot of organizations: these are external tools that read files, call APIs, and act on a user's behalf, largely outside anyone's line of sight. That's a differentiator few competitors match at the endpoint.

Guardrails as the policy enforcement layer

At its core, Bifrost is a policy enforcement layer for AI traffic. Enterprise guardrails validate LLM inputs and outputs, plus MCP tool executions, in real time against configurable rules and profiles:

Rules (CEL-based) define when and what gets validated: inputs, outputs, or both.
Profiles define how that content gets evaluated, and they're reusable across rules for consistent enforcement.

Built-in and integrated guardrail providers include secrets detection (backed by Gitleaks), custom regex and PII templates, Prompt Guardrails (LLM-as-judge), AWS Bedrock Guardrails and a handful of others.

Edge security documentation confirms that every guardrail configured in Bifrost applies automatically to Edge-routed traffic: PII redaction, secrets detection, content safety, policy enforcement, all of it, with no extra configuration needed at the endpoint. A prompt typed into ChatGPT in the browser gets evaluated before it ever leaves the machine, and the same profiles protect Claude Cowork and every other supported surface too.

Remediation options are detect, block, redact, or modify, with three redaction modes (runtime, logs-only, runtime plus reversible logs) built in for compliance-sensitive environments.

Enterprise deployment at scale

Edge rolls out silently through MDM (Jamf, Intune, Kandji) with managed configuration, so admins get a fleet dashboard for devices, approvals, and central config sync. Bifrost Enterprise itself supports high-availability clustering, virtual keys, access profiles, RBAC, audit logs, and In-VPC deployment, which is what makes the gateway-plus-Edge combination workable for regulated industries.

Key takeaway: Bifrost is built for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. It serves as a centralized AI gateway to route, govern, and secure all AI traffic across models and environments with ultra low latency. Bifrost unifies LLM gateway, MCP gateway, and Agents gateway capabilities into a single platform. Designed for regulated industries and strict enterprise requirements, it supports air-gapped deployments, VPC isolation, and on-prem infrastructure. It provides full control over data, access, and execution, along with robust security, policy enforcement, and governance capabilities.

💎 Check out the Bifrost repository ☆

2. 💻 Palo Alto Networks Prisma AIRS: Runtime security for cloud AI workloads

Best for: Enterprises securing AI applications, agents, models, and datasets in cloud and hybrid environments, from development through deployment.

Prisma AIRS is Palo Alto Networks' purpose-built AI security platform, designed to secure the entire AI attack lifecycle. It protects AI and traditional applications, agents, models, and datasets across code, runtime, and cloud layers.

Core capabilities

Prisma AIRS covers four main areas. AI Runtime: Network Intercept provides inline, real-time AI-powered network protection in cloud architectures. AI Runtime: API Intercept embeds Security-as-Code directly in source and scans prompts and model responses programmatically. AI Model Security handles pre-deployment model scanning and registry-level guards. And AI Red Teaming runs automated vulnerability scanning for AI applications and agents.

Prisma AIRS deploys in public clouds (AWS, Azure, GCP), private/on-prem infrastructure, and Kubernetes. Management is centralized through Strata Cloud Manager or Panorama, with deployment workflows and Auto-Execute for rapid provisioning.

According to Palo Alto's product documentation, the platform uses Precision AI to detect prompt injection, sensitive data exposure, malicious URLs, toxic content, and agentic threats (memory manipulation, tool misuse) in real time. SSL/TLS decryption enables inspection of traffic between AI applications and models.

Where it fits: Prisma AIRS excels at cloud-native and runtime protection, especially for organizations building or hosting AI in Kubernetes, VMs, or serverless environments. It complements endpoint-focused tools like Bifrost Edge rather than replacing them.

🌐 3. Netskope: AI Guardrails for SaaS and private AI traffic

Best for: Organizations that need unified runtime defense across sanctioned GenAI SaaS apps, private LLM deployments, and agentic workflows, integrated with whatever CASB/DLP they already have.

Netskope One AI Guardrails provides a dedicated runtime defense layer for AI environments. It analyzes prompts and responses in real time to mitigate prompt injection, jailbreak attempts, and content moderation violations.

What Netskope covers

Netskope scans prompts and responses for a broad set of GenAI apps, including ChatGPT, Claude, Microsoft Copilot, GitHub Copilot, Google Gemini, Amazon Bedrock, DeepSeek, and others, with FedRAMP and PBMM support as well.

Policy configuration happens through the AI Gateway:

Guardrails profiles with predefined categories, keywords, and semantic prompt/response matching (Low/Medium/High confidence).
Enforcement actions: Monitor, Block, or Replace (custom administrator message).
Framework mapping: Detections align with MITRE ATLAS and OWASP Top 10 for LLMs.

The March 2026 Netskope One AI Security suite adds Agentic Broker (visibility and control over agentic AI interactions), AI Gateway (policy enforcement for private/hosted LLMs), and AI Red Teaming, all within the Netskope One platform, with unified DLP and threat protection context tying it together.

Where it fits: Netskope is strongest when AI security needs to sit inside an existing SASE/CASB architecture, particularly for cloud-delivered GenAI SaaS and private AI gateway scenarios. Its guardrails are oriented around the network and cloud path rather than the device itself.

4. ⚙️ Zscaler: AI posture, asset discovery, and AI Guard runtime enforcement

Best for: Security teams that need deep visibility into AI assets across cloud accounts, code repositories, and SaaS, plus runtime guardrails for self-hosted AI applications.

Zscaler's AI Security platform spans two complementary areas documented in their 2026 release notes and configuration guide:

AI Asset Management (posture and discovery)

Onboard AWS, Azure, and GCP accounts for AI resource scanning and data classification.
Discover Embedded AI applications: AI-powered features inside SaaS tools, not just standalone GenAI apps.
GitHub integration for automatic discovery and scanning of repositories for AI models, packages, frameworks, and agentic workflows.
Inventory of GenAI apps, Endpoint AI, MCP servers, agents, and models with AI posture policies.

AI Guard for Apps (runtime enforcement)

AI Guard operates in Proxy and DAS/API modes, inspecting LLM traffic with policy configurations, prompt allowlists, topic/off-topic detectors, and multilingual support. It integrates with LLM providers via proxy endpoints and supports AI Red Teaming with brokers for platforms like Copilot Studio, Agentforce, Amazon Bedrock Agents, and Microsoft Teams.

Where it fits: Zscaler delivers a posture-first, cloud-account-centric AI security model with growing runtime enforcement through AI Guard. It's particularly strong for organizations already standardized on Zscaler platform and needing AI asset inventory plus policy enforcement for their own self-built AI apps.

5. 🔎 Reco: SaaS and AI agent discovery with governance

Best for: Enterprises struggling with shadow SaaS and shadow AI, who need a unified map of apps, agents, identities, and data connections across the third-party ecosystem.

Reco takes a discovery-and-governance approach rather than inline traffic interception. Its platform connects to 260+ SaaS applications and AI tools, building a continuously updated knowledge graph (the "Reco Graph") of identities, permissions, connections, and agent activity.

AI-specific capabilities

Reco's AI Governance module provides:

Discovery: Identifies and catalogs AI tools, from approved Copilot instances to shadow ChatGPT usage, including AI-powered features embedded in existing SaaS apps.
Risk assessment: Evaluates AI integration permissions and data access patterns.
Policy enforcement: Applies governance rules to AI usage without blocking innovation.
Agent security: Maps every agent (who built it, what it can access, where it connects) with real-time threat detection and remediation through existing security stacks.

Reco discovers agents and apps from API, IDP, CASB, browser, and network sources. Its no-code integration engine adds new apps and agent platforms in days rather than quarters. The platform supports agent-ready MCP, API-first architecture, and maps to frameworks including EU AI Act, NIST CSF 2.0, and ISO 42001.

Where it fits: Reco addresses the visibility and SaaS governance layer, basically answering "what AI is running in our environment, and who can it reach?" It's strongest as a complement to inline enforcement tools like Bifrost Edge or Netskope AI Guardrails, especially for organizations with 500+ SaaS apps and shadow AI running wild.

🖋️ How to choose: A layered approach

No single tool covers every AI security surface. The table below maps each platform to its primary strength:

Tool	Primary layer	Endpoint (desktop/browser/coding)	Runtime guardrails	AI asset/posture discovery	MCP governance
💎 Bifrost + Edge	Gateway + endpoint policy enforcement	✅ Native	✅ Profiles + CEL rules	Via fleet inventory	✅ Device-enforced
Prisma AIRS	Cloud runtime + network/API intercept	❌	✅ Inline (cloud)	✅ Cloud asset discovery	Via API intercept
Netskope	SASE/CASB AI guardrails	Via network path	✅ AI Gateway	✅ GenAI app coverage	Limited
Zscaler	AI posture + AI Guard	Partial (Endpoint AI inventory)	✅ AI Guard proxy/API	✅ Cloud + GitHub + Embedded AI	✅ Inventory
Reco	SaaS/agent discovery + governance	Via browser/network sensors	Policy-based (not inline)	✅ Deep SaaS graph	✅ Agent inventory

For most enterprises in 2026, the critical gap is endpoint AI: the ChatGPT tab, the Cursor session, the Claude Code terminal run that never touches a corporate gateway at all. Bifrost Edge is the only platform on this list purpose-built to govern that traffic transparently, with guardrails, app allowlists, and MCP enforcement applied right on the device itself.

Prisma AIRS, Netskope, and Zscaler extend protection into cloud workloads, SaaS paths, and AI asset posture.

The organizations that end up leading on AI security this year won't be the ones that picked a single tool. They'll be the ones building a stack where policy gets defined once, enforced at every layer, and stays visible everywhere AI touches the business.

🔗 Resources:

Bifrost GitHub: https://github.com/maximhq/bifrost
Bifrost Docs: https://docs.getbifrost.ai
Bifrost CLI: npx -y @maximhq/bifrost-cli

Thanks for reading this article! ❤️

I'd love to hear your thoughts on this mode in the comments!