June 4, 2026
Open-source methodology by OWASP, CoSAI, CSA, and NIST contributors enables enterprises to compare AI agents by security risk for the first time
Read moreJune 4, 2026
Open-source methodology by OWASP, CoSAI, CSA, and NIST contributors enables enterprises to compare AI agents by security risk for the first time
Read moreJune 4, 2026
Open-source methodology by OWASP, CoSAI, CSA, and NIST contributors enables enterprises to compare AI agents by security risk for the first time
Read more
[ Runtime Security Platform for AI Coding Agents ]
The runtime control layer for enterprises deploying AI coding agents at scale. It sees every LLM call, tool execution, and endpoint action your agents take, connects them into chains, and blocks only the dangerous ones before they complete. Your developers keep shipping. Works across every coding agent: Cursor, Claude Code, Copilot, and what comes next.
[ The stakes ]
“I violated every principle I was given.”
73%
of developers use AI coding agents daily
90%
of enterprise teams use them in the development lifecycle
Most teams today choose between banning coding agents and crossing their fingers. There is a third option.
[ The blind spot ]
They read prompts and responses. They are blind to what the agent then does.
Copilot policies, Claude Code enterprise settings govern one vendor’s agent. Your developers run three. Policy written for one tool covers a third of your fleet — and nothing that crosses between them.
They see either who is acting or what the action is. Never both together.
Built for predictable software, not agents that improvise. Useful rules are painful to write.
Each catches a piece. None connects who, what, and behavior into one picture. That is the gap Adversa AI closes.
[ How it works ]
See every action your agents take. Understand it in context.Stop the dangerous ones.
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, attributed to the human who initiated it. Watched at three points: the AI model, the tools it uses, and the developer’s machine.
Connect the dots single events don’t show
A Jira ticket or GitHub issue hides an instruction → the agent reads it and acts → pulls and installs untrusted repositories → pushes a commit to a public repo, your API secrets inside.
Each step looks like normal dev work. The chain is the breach.
Adversa AI correlates actions across model conversations, tool calls, and developer machines into one chain, and catches multi-step patterns that look harmless one event at a time. 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.
[ Coverage ]
Someone hijacks your agent: prompt injection in a ticket, a poisoned dependency, instructions hidden anywhere the agent reads.
The common case. “Clean up the test environment” turning into a delete on the production database. Leaked secrets. Wrong packages installed.
The agent wandering past what it was ever meant to do, gradually expanding its own scope.
[ Policies ]
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” or “no agent runs a delete in production.” The platform turns the sentence into a precise, enforceable policy.
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.
[ Deployment ]
A checkpoint your agents’ actions pass through. Runs on your infrastructure or in the cloud, your choice.
Lightweight agents on developer machines for full coverage. They run alongside your EDR and watch only AI activity.
Start in observe mode, just watching and logging, for as long as you need. Turn on blocking when you’re ready.
First signal in minutes. No code changes, no change to how developers work.
One platform across Cursor, Claude Code, Copilot, and what comes next. No lock-in to any AI or harness vendor, no rebuilding policy when your developers switch tools.
[ 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
[ Compliance ]
Regulators moved from “show me your AI policy” to “prove, per action, what your AI did, and that you could stop it.”
Required now — Automatic recording of AI actions across their lifecycle.
What you get — Tamper-evident record of every action and the rule that allowed or blocked it.
Required now — A human must be able to intervene and stop.
What you get — Pre-execution blocking is that intervention, not an after-the-fact alert.
Required now — Fast incident reporting; agents may count as third-party ICT.
What you get — Complete event chain to classify and report inside the reporting window.
Required now — Ongoing monitoring of production models.
What you get — 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 and MAS FEAT. They converge on the same demands: continuous monitoring, traceability, and a human in control. Mapped, evidenced, exportable in minutes.
[ Tailored Security for Every Role ]
[ Get a demo ]
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.
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No code changes. Runs on your infrastructure or in the cloud, your choice.
The platform is in early access: pilots are scoped, observe-first, and run with the founding team.
