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)
[ Coding Agent Security Platform ]
Runtime security for the agents that write your code
AI detection and response (AIDR) is runtime security for AI: it watches what AI agents do and steps in when the behavior is wrong. AIDR observes the model’s prompts and responses, the tool and MCP calls, the agent’s endpoint actions, and the data it touches. Adversa AI builds AIDR for coding agents, Claude Code, Copilot, Cursor, Codex, and what comes next. We stop the damage before it lands: wiped databases, leaked credentials, malicious code shipping to production.
Your controls ask yes or no questions: malicious file, authorized user, allowed destination. An agent’s risk has no such answer. Three properties of agents are why:
An agent that reads customer records in one step and sends a summary to an outside address three steps later may have been authorized to do both. Each action passes all checks individually. The chain of events is the breach.
Prompts, retrieved documents, tool responses, README files, web pages: any of them can carry text that turns into a command and redirects the agent mid-session, toward actions its policy allows but its task never required.
The agent inherits the developer’s permissions and acts at machine speed. Nothing is stolen, so identity and endpoint controls stay quiet.
AIDR judges behavior against purpose, across the whole chain.
[ Adversa AI decision graph ]
A poisoned repo or skill gets installed. The agent ingests it and starts acting on an attacker’s instructions. It reads a secret. It pushes a commit — your API keys inside. Every filter you run said yes four times: DLP saw a normal commit, EDR saw a developer’s process, the gateway saw a routine completion call. No single event was suspicious, but the chain of events became the breach.
The platform builds an agent decision graph: every action, its inputs and its effects. Then it scores risk across the chain as it forms, across the model, the tools, and the machine. When the chain crosses the line, it’s blocked before it completes. That is detection and response at the level agents actually operate: the chain, not the event, and inline, not after the fact.
Attacks are less frequent than mundane mistakes and agentic misbehavior. The same chain analysis catches all three sources of damage.
[ 01 ]
“What else is the agent doing?”
Agents gradually expanding their own scope, acting far beyond the original task, runaway loops burning compute and tokens.
[ 02 ]
“Can one wrong command drop production?”
A “test cleanup” that drops the production database; a quick commit carrying your secrets into public code; a key pasted into a prompt.
[ 03 ]
“Can someone hijack your agent?”
Prompt injection and malicious context: a poisoned repo or skill, a rogue MCP server, and the exfiltration chain each one starts.
[ How the Adversa AI platform works ]
An AI detection and response platform collects from every place an agent acts, judges each session as a chain, enforces inline, stores the evidence. How we do it for coding agents:
Full visibility across every agent surface
See what the coding agents you’ve approved actually do. Every model call, every tool call, every file touched, every outbound connection. Watched at three points: the AI model, the tools it uses, and the developer’s machine.
Any agent, any harness, CLI tools, IDEs, CI — one capture layer. You don’t have to know how each vendor’s protocol behaves.
Connect the dots single events don’t show
Each step looks like normal dev work. The chain is the breach.
The decision graph correlates model conversations, tool calls, and endpoint actions into one picture, and scores each chain as it builds. Single-event tools wave each step through, because each step on its own is fine.
Block the chain before it completes
Rules score risk across the linked chain as it builds, not one action at a time. When the score crosses the line you set, the chain is blocked before it completes. The developer gets a clear error and keeps working. Enforcement is yours to scope: per policy, per environment, per team.
No naked alerts: every detection ships with its chain, the rule that fired, and the exact evidence behind it.
AIDR provides the coverage layer your older tools were never built for, and it feeds each of them. What each one sees, and what AIDR adds:
| What you already run | What it sees | What AIDR adds |
|---|---|---|
| LLM proxy / AI gateway | One request at a time, and only the traffic routed through it. The right place for inbound prompt inspection, and a good collector. | Memory across the whole session, and coverage where no gateway sits: developer laptops and CI. Adversa AI reads from your gateway, or runs as one. |
| EDR / XDR | The process: an agent process wrote a file or opened a socket. | The reason: the prompt-to-action chain back to the poisoned document three steps earlier. Verdicts land in your EDR and SOC as high-fidelity detections. |
| SIEM / SOC | Everything, afterwards. Log-based detection takes minutes to hours; an agent’s chain completes in seconds. | The inline decision point and a high-fidelity telemetry source. Your SIEM stays the system of record; every event ships to it with the full chain attached. |
| DLP | Files and channels. | Data leaving inside a prompt, a tool argument, an encoded payload, or an agent-to-agent handoff. Secrets flagged at the tool call and blocked under policy before they leave. |
| Firewall / NGFW | Destinations. | Content and intent of a call to an approved destination, which is where exfiltration usually goes: a sanctioned SaaS endpoint or an image URL. |
| SAST, SCA, secret scanning | The code, before deployment. | The agentic work that ends before CI/CD: secrets theft and database deletion happen before a commit. |
| IAM / identity | What the agent may do. | What it actually did, under the credentials it inherited, even when the actor is a persistent agent identity rather than a person at a login. |
AIDR is the layer that judges what happens inside the session, and it replaces none of the tools above.
The category is young and crowded with re-labels: a rebadged gateway, posture management, managed detection with an AI feed. Eight questions separate detection and response for agents from logging with a new name. Our answers sit under each; hold every vendor to the same eight.
Many products do one and name the category. Our answer: collection across three surfaces, chain-level detection, inline enforcement, and evidence-backed response for the coding agents you’ve approved, with coverage reporting across them. We don’t lead with discovery of unknown tools.
Homegrown AI apps, SaaS business agents, and agents on endpoints are three different engineering problems; most vendors are strong in one. Our answer: the endpoint problem, meaning coding agents on developer machines and in CI, every brand in one view. The hardest of the three, and where the agent holds the most authority.
Ask to see a cross-tool action chain reconstructed end to end on a developer machine. Our answer: the agent decision graph is the unit of detection; the first pilot readout shows it.
Keyword and regex detection fails on reworded injection and slow-building attacks. Our answer: content is one signal among trust state, action type, identity, and provenance. A reworded attack trips the same rule.
Inline blocking on a working agent needs warn-first and a human in the loop for high-impact actions. Our answer: warn, deny, or human-in-the-loop, per policy, per environment, per team, switched on over the would-have-blocked evidence.
Inline inspection sits in the user’s path. Our answer: typical checks add under a millisecond, the heaviest paths under 50 milliseconds. The design fails open.
Full session capture can mean prompt content in your logs. Our answer: action telemetry focused on the agent itself, redacted session fragments necessary to reconstruct agentic actions, secrets never stored. When we read from your gateway, you control what we receive. The solution can run in the cloud or on-prem; in the latter case we retain nothing.
A framework mapping is how you check a coverage claim. Our answer: 100+ attack-anchored defaults, maintained by the offense team that co-authors OWASP’s LLM and Agentic Top 10. Ask for the per-policy mapping in the pilot.
Two components with a single dashboard and centralized policy engine.
Intercepts the agents’ own traffic: LLM calls, responses, tool executions, and MCP calls. Inline, or reading from the LLM/API gateway you already run.
Watches actions on the machine: files, shell, CI/CD. Strictly the agent’s actions, not the developer’s.
Every action is logged into the agent decision graph and tied to the run that produced it: which agent, doing what, triggered from where. Each action is scored and gets its own verdict, the chain is scored as a whole. The response follows your policies: warn, deny, or human-in-the-loop.
[ Adversa AI policy engine ]
Our red team maintains the rulebook, built on years of breaking AI systems. You start covered, without writing a thing.
Say “these developers’ agents can’t touch AWS”. The platform turns the sentence into a precise, enforceable policy — and it can turn a screenshot of an AI incident into one, too. Generated policies are validated, simulated, versioned, and approved before they go live.
Detecting malicious inputs purely by their wording is brittle: adversaries iterate faster than rules, and natural language has infinite surface area. We use content as one signal alongside trust state, action type, identity, and provenance, and match the shape of dangerous behavior. An agent is only as trusted as the riskiest thing it just touched: read an outside web page, ticket, or dependency, and it loses the right to open your secrets or call out to the internet — automatically. A reworded attack trips the same rule. Policies you write today still hold against attacks invented next year.
[ 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
[ See it yourself ]
Watch the platform turn individual agent actions into chains, stop the dangerous ones before they become the incident, and produce the evidence your auditors will ask for. Start with a scoped, observe-mode pilot: one dev team, the agents you’ve approved. See the would-have-blocked list on your own traffic, turn on blocking when ready.
Form not loading? Open it in a new tab.
Runtime security for AI: it watches what AI systems and agents actually do, across prompts, model responses, tool and MCP calls, endpoint actions, and the data it touches, and enforces policy at the point of execution. The unit of detection is the chain of actions judged against the agent’s purpose, not a single event judged against a signature.
No. It is a coverage layer they don’t yet address: the session, the chain, and the inline decision before an action completes. AIDR events feed your EDR and SIEM; response workflows stay where they are.
A gateway inspects one request at a time and only the traffic routed through it: the right place for inbound prompt inspection and a good collector, which is why the platform reads from yours. AIDR adds memory across the session, the tool and endpoint actions a gateway never sees, and coverage on laptops and CI runners where no gateway sits. No gateway yet? The agent security gateway runs as one.
Posture management tells you which agents exist and what they may do. AIDR tells you what they did and stops the dangerous part. The two form a loop: posture lets you tune runtime policy; runtime observations help update posture. The coverage report is our runtime half of that loop.
They govern one agent, their own, and only through its configuration. Your developers run three brands side by side, and the chain that matters crosses between them. Even a single agent brand no longer means a single model: model routers split calls across providers, the complex work to one model, the cheap work to another. A provider can only secure the traffic it sees, and after routing, none of them sees it all.
They will ship a layer, and it will see their own telemetry first. Your agents already run with production credentials today, and coding agents on endpoints are the hardest of the three coverage problems, where specialist depth matters most. AIDR is additive, so nothing bought now has to be ripped out. Monitoring now, blocking on your highest-privilege agents: that interim position is exactly what the pilot delivers.
The endpoint one: coding agents on developer machines, in IDEs and CLIs, and in CI, across Claude Code, Copilot, Cursor, Codex, and what comes next. SaaS business agents and homegrown AI apps are different engineering problems and not what we build for.
Not if it is graduated: policies warn before they block, high-impact actions can require a human in the loop, and blocking is scoped per policy, per environment, per team. Observe mode shows the would-have-blocked list and its false positive rate before anything is enforced, and a crash never blocks a developer.
We collect action telemetry focused on the agent itself, plus the redacted session fragments needed to reconstruct agentic actions. Secrets are never stored, and what we ingest is documented in the trust pack; when we read from your gateway, you control what we receive. The solution can run in the cloud or on-prem; in the latter case we retain nothing.
The runtime half of one. The record of every action and the rule that allowed or blocked it is what EU AI Act Art. 12 asks for; pre-execution blocking is the human intervention of Art. 14; the complete event chain is what DORA’s reporting clock needs. The mapping lives on the risk and compliance page.
The market expects an inventory step. We observe and control the agents you’ve sanctioned and routed; coverage reporting shows which approved agents are enrolled and where the gaps are. We don’t lead with discovery claims.
Every default policy is anchored to a confirmed attack technique, maintained by the team that co-authors OWASP’s LLM and Agentic Top 10. Ask for the per-policy mapping in the pilot; it is how you check any vendor’s coverage claim, including ours.
The agents you’ve approved — Claude Code, Copilot, Cursor, Codex, and what comes next. Policies hold when developers switch tools.
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.
