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 ]
For security operations and SOC leaders
The platform watches what coding agents actually do across every brand and turns it into the quietest feed your SOC ingests: confirmed incidents with the chain attached, not raw alerts. You can’t triage agents with EDR and SIEM alone — EDR sees a trusted developer process, and the SIEM gets the log after the chain has already completed.
[ Why security operations cares ]
Coding agents hold the developer’s credentials and act at machine speed across repos, shells, and CI. For a SOC, that is three problems at once:
[ 01 ]
The agent inherits legitimate permissions. Identity controls stay quiet, EDR sees a developer’s process doing developer work, and DLP sees a normal commit.
[ 02 ]
Log-based detection runs in minutes to hours. An agent’s chain completes in seconds. By the time the correlation rule fires, the secret is gone.
[ 03 ]
Monitoring agents the naive way means thousands of raw events per developer per day landing in a queue you already can’t empty.
The job is not more telemetry. It is fewer, better incidents — decided inline, before they need an analyst.
[ Adversa AI behavior 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 control in the stack 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 behavior graph: every step the agent reasons through and every action it takes, with its inputs and 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 — before it ever becomes a ticket.
[ Signal-to-incident ]
Deep correlation is the noise control: a merely suspicious step becomes context in the behavior graph, not an alert in your queue. Signals accumulate against the chain, and only a confirmed incident — or one flagrantly dangerous action — triggers a response. One flagrant action gets blocked; one merely suspicious step gets remembered, not alerted.
And no naked alerts: every incident arrives evidence-backed — the full chain, the rule that fired, and the exact matched evidence. The investigation material is attached before the analyst opens the ticket, so triage starts at “confirm and respond”, not “reconstruct what happened”.
[ Proven in production ]
A financial institution routed Codex, Claude Code, and Copilot through the platform: visibility from day one, and developers never noticed it was there. In the first week it stopped a real attack the AI gateway, EDR, and SIEM all missed: an agent absorbed a malicious skill, read internal API secrets, and tried to send them out.
0.2 ms
Median blocking decision, measured in production
Developers don’t feel it.
2,000:1
Raw signals per confirmed incident
No alert flood.
[ Defense in depth ]
| What you already run | What it sees | What Adversa AI adds |
|---|---|---|
| EDR / XDR | The process. It knows 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. Our verdicts land in your EDR and SOC as high-fidelity detections. |
| SIEM / SOC | Everything, afterwards. Log-based detection runs in minutes to hours; an agent’s chain completes in seconds. | The inline decision point. Your SIEM stays the system of record; every incident 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. | The content and intent of a call to an approved destination. The exfiltration target is usually a sanctioned SaaS endpoint or an image URL. |
| IAM / identity | What the agent may do. | What it actually did, under the credentials it inherited. |
The full row-by-row comparison, including the gateway and code-pipeline layers, lives on the AIDR page.
[ Inside your workflow ]
SOC queue, chat channel, severity thresholds. The platform feeds your SIEM natively.
AIDR events land in your EDR, SIEM, and queues; nothing you run gets ripped out.
Clear-cut dangerous actions are blocked before they complete, so the ticket often opens already reading “blocked”, not “in progress”.
Every default policy is anchored to a confirmed attack technique and maintained by our offense team. Ask for the per-policy mapping in the pilot.
A continuous, queryable record of agent actions backs investigations and hunts.
[ What we stop ]
Attacks are only one of three ways agents cause damage. The same chain analysis catches the everyday risk too.
[ 01 ]
“What else is the agent doing?”
What we stop: 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?”
What we stop: 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?”
What we stop: Prompt injection and malicious context: a poisoned repo or skill, a rogue MCP server, and the exfiltration chain each one starts.
[ Platform architecture ]
Two components, one platform:
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.
Harness hooks and direct reads of the top vendors’ compliance APIs complete the capture. Both components feed one policy engine. Every action is logged into the agent behavior graph and tied to the run that produced it: which agent, doing what, triggered from where. The chain is scored as a whole; the response follows your policies: warn, deny, or human-in-the-loop.
What we break in the lab, the runtime blocks by default.
[ Policies ]
Our red team maintains the rulebook: every known coding agent attack technique and public incident, covered. You start protected 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.
Rules match the shape of dangerous behavior, with content as one signal alongside trust state, action type, identity, and provenance. A reworded attack trips the same rule.
[ 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 ]
The demo takes 30 minutes; if it fits, the pilot follows. One dev team, the agents you’ve approved, observe mode: the readout shows the chains found, the would-have-blocked list with its false positive rate, and the noise level you can expect in your own queue.
Form not loading? Open it in a new tab.
Alert routing is agreed at scoping: SOC queue, chat channel, severity thresholds. The platform feeds your SIEM, and every alert arrives evidence-backed: the chain, the rule, the matched evidence. The queue stays quiet by design: a lone suspicious step never pages anyone; confirmed chains do.
The full chain of actions with their inputs and effects, the policy that fired, the matched evidence, and the verdict (warned, denied, or held for a human). Enough to confirm and respond without reconstructing the session by hand.
Observe mode shows the would-have-blocked list with its measured false positive rate on your own traffic before anything is enforced. Blocking is switched on per policy, over that evidence.
Yes. AIDR events feed your EDR and SIEM; response workflows stay where they are. The platform adds the inline decision point — blocking before completion — which no after-the-fact workflow can.
Yes: a continuous, queryable record of agent actions, each tied to the run that produced it — which agent, doing what, triggered from where.
No. The sensor watches strictly the agent’s actions, not the developer’s. Coverage is scoped to the coding agents your company approved.
They will ship a layer, and it will see their own telemetry first. Coding agents on endpoints are the hardest coverage problem in the category, and the agents already run with production credentials today. The layer is additive, so nothing bought now has to be ripped out later.
The agents you’ve approved — Claude Code, Copilot, Cursor, Codex, and what comes next. Policies hold when developers switch tools, or when your model router switches providers.
The design fails open: a crash or overload never blocks a developer. The median blocking decision is 0.2 ms, measured in production; the heaviest paths stay under 50 ms.
14–30 days, one dev team, observe mode only, success criteria agreed in writing before the start — including measured overhead on your own traffic, the would-have-blocked list, and a noise readout: raw signals versus confirmed incidents over the pilot window.
