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 risk, compliance, and audit teams
The platform records what every approved coding agent actually does and the rule that allowed or blocked each action — the per-action record that AI rules now ask for. A policy document no longer answers the question: regulators moved from “show me your AI policy” to “prove, per action, what your AI did, and that you could stop it”.
[ Why risk and compliance cares ]
Coding agents act with developer credentials on systems that reach production, and they entered the company as developer tooling, not as a reviewed risk. Three questions arrive on your desk regardless:
[ 01 ]
The auditor
AI-specific requirements only grow, and they converge on the same demands: continuous monitoring, traceability, and a human in control.
[ 02 ]
The regulator
DORA-class windows need a complete event chain fast — and coding agents may count as third-party ICT.
[ 03 ]
You
Coding agents hold production-reaching permissions. An entry with no control and no evidence is the gap every review finds.
One record answers all three.
[ 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. The agent was authorized for every step; 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. And when something does go wrong, the same chain is what you hand the auditor: what happened, what was allowed, what was blocked, by which rule.
[ 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.
[ What we stop ]
Cyberattacks 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.
[ Compliance mapping ]
| Framework | Required now | What you get |
|---|---|---|
| EU AI Act, Art. 12 | Automatic recording of AI actions across their lifecycle. | A record of every action and the rule that allowed or blocked it. |
| EU AI Act, Art. 14 | A human must be able to intervene and stop. | Pre-execution blocking is that intervention, not an after-the-fact alert. |
| DORA | Fast incident reporting; agents may count as third-party ICT. | A complete event chain to classify and report inside the reporting window. |
| SR 11-7 | Ongoing monitoring of production models. | A continuous, queryable record of model-driven actions. |
NIST AI RMF, ISO 42001, SOC 2, Japan’s AI Guidelines for Business, Singapore’s Model AI Governance Framework and MAS FEAT. They converge on the same demands: continuous monitoring, traceability, and a human in control. Mapped, evidenced, exportable in minutes.
[ The record ]
[ Our own posture ]
A control becomes part of your audit surface, so our posture comes first: SOC 2 Type I & II audited, ISO 27001 certified, GDPR compliant. Deployment where your policy requires — cloud or on-prem, per customer. Secrets are never stored, and what we ingest is documented in the trust pack.
[ 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; the chain is scored as a whole; the response follows your policies: warn, deny, or human-in-the-loop.
[ 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. Generated policies are validated, simulated, versioned, and approved before they go live — a change-controlled path from requirement to control.
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, a scoped observe-mode pilot follows: one dev team, the agents you’ve approved. The readout includes the record itself — every action, the rule, the verdict — export-ready, alongside the would-have-blocked list that justifies turning enforcement on.
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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.
Scoping is your counsel’s call — it depends on how your organization’s AI use is classified. What the platform supplies either way is what Art. 12 and Art. 14 describe: automatic recording of AI actions, and a human able to intervene and stop before execution.
Incident reporting runs on a clock, and agents may count as third-party ICT. The platform gives you the complete event chain to classify and report inside the window, with the rule and the verdict attached.
The sensor watches strictly the agent’s actions, not the developer’s, and coverage is scoped to the coding agents your company approved. That design boundary is documented — it is the distinction the works-council conversation needs.
Cloud or on-prem, per customer; on-prem, we retain nothing. We collect action telemetry, not your codebase; secrets are never stored, and what we ingest is documented in the trust pack.
The event stream to your SIEM with the full chain attached; the action record, export-ready for audit; and framework mappings, evidenced and exportable in minutes.
We cover the sanctioned fleet: the agents you’ve approved and routed. We don’t discover unknown tools across the estate — coverage reporting shows which approved agents are enrolled and where the gaps are, so your risk register reflects what is actually controlled.
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.
Yes — pilots start in observe mode: watch, log, and review the would-have-blocked list with its false positive rate on your own traffic. When you turn enforcement on, policies can warn before they block, or require a human in the loop. Blocking is per policy, when you decide.
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.
