June 4, 2026
100 enterprise AI agents rated, plus an open source methodology you can use in procurement and security posture tracking.
Read more: AIRQ: AI Risk Quadrant — independent AI agent security rating (opens in new tab)June 4, 2026
100 enterprise AI agents rated, plus an open source methodology you can use in procurement and security posture tracking.
Read more: AIRQ: AI Risk Quadrant — independent AI agent security rating (opens in new tab)June 4, 2026
100 enterprise AI agents rated, plus an open source methodology you can use in procurement and security posture tracking.
Read more: AIRQ: AI Risk Quadrant — independent AI agent security rating (opens in new tab)
[ Buyer’s guide ]
The AI security field is split into a dozen separate market segments. Pick the segments you need by asking who built the AI and where it runs, then which job you need done: find it, stop risky actions, catch attacks, or test before launch. This guide maps the segments, the questions to answer internally, and what to ask each vendor.
Prompt injection may never be fully solved: the UK’s National Cyber Security Centre and OpenAI both say so. Limit what each agent can reach and do before you buy tools that filter what it reads and says. The order that works:
If not, start with the find job in every situation, before you buy anything else. You can’t protect AI you haven’t listed.
Built by us, by a vendor, or by staff on a no-code platform; running on a laptop, inside a SaaS platform, or in our own cloud. The answer places it in one of the four situations on the map, and the situation says which segments to review.
Its own identity, a shared account, or an employee’s login. The answer sets how much damage a tricked agent can do, and points to agent identity and access and to data security for AI.
Outside content means web pages, email, tickets, or customer chats. An agent that does all three needs hard limits, not just filters: its own narrow access, gateway rules, a sandbox, and human approval for risky actions.
Check your security platforms and your Microsoft, Google, or AWS licenses before adding a vendor. Most large security platforms bought AI security startups in 2025 and 2026, so some coverage may already be paid for.
The answer decides whether AI detection and response (AIDR) and AI governance tools pay off now, or only once someone owns the response and the evidence auditors, regulators, or customers will ask for.
Place every AI use in one of four situations, set by who built it and where it runs. The situation tells you which segments of the AI security market to review first, and which to add next. Each segment does one or more of four jobs: find what exists, stop risky actions, catch attacks across a session, or test before launch.
| Whose AI, and where it runs | Review first | Then add |
|---|---|---|
| A. Employees use AI apps (ChatGPT, Copilot Chat, AI browsers) | AI usage control | Data security for AI |
| B. Agents on employee laptops (Claude Code, Codex, Cursor) | Agentic endpoint security; AI detection and response (AIDR) | AI supply chain security; agent sandboxing |
| C. No-code agents staff build (Copilot Studio, Agentforce) | AI security posture management (AI-SPM); data security for AI | Agent identity and access |
| D. Agents your teams build (Your products and internal apps) | Agent identity and access; AI and MCP gateways | Runtime protection; AI red teaming; agent sandboxing |
| Across all situations | AI governance: one inventory, policies, and evidence | |
Match the vendor to the problem before you compare features.
Many AI security products are an existing product with a new job title. Each is a good tool for its own job; the risk is buying it for yours. Three relabels to recognize:
An LLM proxy with prompt filters, sold as agent security. It inspects one request at a time, and only the traffic routed through it: no tool executions, no endpoint actions, no memory across a session.
An inventory of AI assets and their permissions, sold as protection. Posture shows what an agent may do. It can’t show what the agent did, and it can’t stop an action in flight.
An EDR, SIEM, or MDR that now ingests AI logs. It cannot match the agent’s reasoning and assigned task to its actions, and enforcement often comes after human review: too late, when the damage is done.
Ask where a product watches, not what it is called.
Ask every vendor the same two questions, then the questions for its segment. The answers in the table’s right-hand column should end the conversation.
[ 01 ]
What exactly will a 30-day pilot measure, before we sign? Walk away if the only evidence is the vendor’s own test results.
[ 02 ]
Which of our apps, clouds, and devices are covered now, and which are on the roadmap? Walk away at “all”, “every”, or “complete”.
| Segment | Ask | Walk away if you hear |
|---|---|---|
| AI usage control | Where do you see AI use: browser, network, or device, and what does each miss? Can you redact or warn in the moment, not only block or log? | “Complete visibility into every AI tool”; “eliminates shadow AI” |
| Agentic endpoint security and AIDR | What do you see and stop on the laptop itself: file changes, commands, local add-ons? Beyond alerts, can you stop a single action, or switch an agent off today? Can you enforce agent settings and approved add-ons on every device, and recheck them on each update? | A network-only product claiming to cover local agents; “safe auto-approve” |
| AI security posture management (AI-SPM) and data security for AI | Which agent platforms do you connect to today, and do fixes reach each agent’s owner? How much of our data do you actually scan, and how often? | “Agentless full visibility”; “real-time” built on periodic scans |
| Runtime protection: guardrails and AIDR | Against an attacker who adapts and keeps trying, what gets through on our agent, and how much normal traffic do you wrongly block? Beyond alerts, can you stop a single action, revoke access, or switch an agent off today? | “99% detection”; “stops prompt injection”; “zero latency” |
| Agent identity and access | Does each agent get its own identity, a named owner, and short-lived access we can revoke everywhere at once? Do logs name both the agent and the person it acted for? | “Zero standing privilege for all agents”; “intent-based authorization” |
| AI and MCP gateways, AI supply chain security | Is everything blocked unless approved, matched on exact identity rather than name, and approved again when it changes? What do you check after install? | “Verified safe”; “scanned means safe”; “the gateway prevents prompt injection” |
| Agent sandboxing | Is outbound traffic, including DNS, blocked by default and enforced from outside the sandbox? What outside the sandbox later runs what the agent wrote? | “Complete isolation”; “escape-proof” |
| AI red teaming | Do you test our deployed agent with its real tools, after every model or prompt change? Are you independent of our guardrail vendor? | “Certified safe”; attack counts offered as proof of quality |
| AI governance | Which obligations do you map, what evidence do you produce, and do you pull in our security tools’ findings? | “One-click EU AI Act compliance” |
Most enterprises end up running two to five of these segments, and agent incidents happen in the gaps between them.
[ Coding Agent Security Platform ]
Agentic endpoint security and AI detection and response (AIDR) for situation B: AI coding agents on employee laptops and in CI. Claude Code, Copilot, Cursor, Codex, and what comes next, in one view.
The agent’s own actions on the machine, through our endpoint security sensor: file changes, shell commands, CI/CD steps. Our agent security gateway adds the model calls, tool executions, and MCP calls.
A single action or a whole chain, inline, before it completes: warn, deny, or require a human in the loop, per policy, per team, per environment. 100+ attack-anchored policies ship by default.
The platform doesn’t cover situations A, C, or D: no usage control for chat apps, no posture management for no-code agents, no guardrails for the agents you build. We don’t lead with discovery of unknown AI tools, and we don’t replace your gateway, EDR, or code scanners: we read from them and feed them.
Adversa AI started in AI security back in 2020 and has run an award-winning specialist AI red teaming service ever since. The same offense team that co-authors OWASP’s LLM and Agentic Top 10 writes the default rules for our Coding Agent Security Platform.
[ Trust & Proof ]
Adversa AI experts are co-leads and core members of industry-defining frameworks and initiatives: NIST AI RMF, OWASP ASI, CoSAI, CSA AI CM. Trusted by Fortune 500 enterprises including top banks, insurance companies, fintech, Big Four, and automotive enterprises.
Analyst recognition
Standards leadership
A decade of adversarial research
20+ industry awards
20+ industry awards
[ Put us on your shortlist ]
Start with a scoped, observe-mode pilot: one dev team, the agents you’ve approved, success criteria agreed in writing before the start. You leave with a would-have-blocked list from your own traffic to hold every other vendor against.
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There is no single leader, because AI security is about a dozen separate market segments: AI usage control, data security for AI, agentic endpoint security, AI detection and response (AIDR), AI supply chain security, agent sandboxing, AI security posture management (AI-SPM), agent identity and access, AI and MCP gateways, runtime protection, AI red teaming, and AI governance. Most large security platforms have bought AI security startups since 2025, so most segments hold both specialists and platform vendors. Find your situation on the map first, then shortlist the AI security companies that lead the segments it points to.
That is situation B, an agent on an employee laptop. Review agentic endpoint security, AIDR, and AI supply chain security. First move: lock the tool’s admin settings, with the sandbox on, no “approve everything” mode, and approved add-ons only. Pages that go live get the normal web security review.
That is situation A, employees using AI apps. Review AI usage control. First move: allow approved AI apps, and redact or warn on sensitive data instead of blocking everything.
That is situation C, no-code agents staff build. Review data security for AI and AI security posture management (AI-SPM). First move: fix files that are open to everyone before the rollout widens.
That is situation D, an agent your teams built. Review agent identity and access and AI and MCP gateways, then runtime protection and AI red teaming. First move: give the agent its own limited access, a refund cap, and human approval above it.
That is situations B and D. Review AI supply chain security and AI and MCP gateways. First move: an approved list only, with every update approved again before it runs.
Find the situation first, then ask every vendor the same two questions: prove it on our agents and our data before we sign, and show coverage today, platform by platform. Then use the segment questions, and walk away from claims like “stops prompt injection” or “complete visibility”.
The agents and surfaces in scope (IDE, CLI, CI, MCP servers); the situation on the map; the response modes you need (observe, warn, block, human approval); latency and fail-open requirements; data handling and deployment model, cloud or on-prem; SIEM and EDR integration; the frameworks the detections must map to, such as the OWASP Top 10 for LLM and Agentic Applications and MITRE ATLAS; and a pilot with success criteria agreed in writing.
A gateway inspects model traffic one request at a time, and only the traffic routed through it. An AIDR platform watches what agents do across the whole session, tool and MCP calls, endpoint actions, and the chain they form, and enforces at the point of execution. For agents on laptops, ask where an AIDR product watches: one that reads traces from your apps or sits on the network can’t see local file changes or commands.
Open source scanners, guardrail libraries, and policy engines are a good way to learn the problem and cover one surface. The cost is maintenance: attack techniques against agents change monthly, and somebody has to keep detections current, correlate across surfaces, and run enforcement in the developer’s path without slowing it. A commercial tool earns its price when the vendor’s team does that work for you.
We cover situation B: agentic endpoint security and AIDR for AI coding agents on employee laptops and in CI, across Claude Code, Copilot, Cursor, Codex, and what comes next. The platform doesn’t cover situations A or C, and we run AI red teaming for the agents you build as a separate service.
14–30 days, one dev team, observe mode only, success criteria agreed in writing before the start — including measured overhead on your own traffic and the would-have-blocked list that justifies turning blocking on.
