Don't just find your AI. Prove it's governed.
AI governance is now a board obligation. SIEGE turns it into an outcome: every AI system inventoried, mapped to NIST AI RMF, ISO 42001, and the EU AI Act, and continuously tested under attack.
Every company became an AI company. Almost none became AI-governed.
Copilots, models, and agents entered your organization faster than governance could follow, most without an owner, a risk tier, or a single tested control. Now the rules have caught up: the EU AI Act carries fines up to 7% of global revenue, and NIST AI RMF and ISO 42001 have become the bar auditors measure against.
The uncomfortable question isn't whether you use AI. It's whether you could prove, today, that any of it is governed. A policy binder can't answer that. Only tested controls and dated evidence can.
One control set. Every framework you answer to.
Four functions, one continuous loop.
Compliant on paper. Monitored in reality. Both, always.
Every AI system classified, controlled, and backed by evidence your auditor accepts.
- Full AI inventory, including shadow AI
- Risk tiers aligned to the EU AI Act
- One control set crosswalked to NIST AI RMF and ISO 42001
- Dated, board-ready evidence and reporting
Continuous adversarial validation, the same methodology that tests DoD networks, running against your AI.
- Controls tested for prompt injection, data leakage, and model abuse
- New AI systems auto-inventoried and queued for validation
- Evidence refreshed as your estate changes, not once a year
- Every failure comes with the exact path an attacker would take
The AI took an action. Can you prove it was authorized?
Every AI governance framework being enforced today — the EU AI Act, NIST AI RMF, ISO 42001 — shares a common requirement: human oversight. Not as a principle. As a documented, auditable fact. The question regulators are training themselves to ask isn't whether your AI system has policies. It's whether you can prove, for any specific action the system took, that it operated within authorized boundaries.
SIEGE's Cyber Control Plane is built to answer that question. Every agent action flows through a governance layer you define and own: the systems the agents may reach, the techniques they may use, the thresholds that require human approval before escalation. The agents operate inside those parameters. Everything outside them is off-limits by architecture — not by policy document. What results is a complete, dated audit trail, action by action, authorization by authorization, ready for your AI governance team, your CISO, and your external auditors the moment they ask.
Define exactly which systems, networks, and techniques fall within the assessment boundary. The agents operate inside it. Nothing outside is touched, accessed, or affected — by design, not by policy.
Set escalation thresholds that require explicit human authorization before agents proceed to higher-risk techniques. You are always in the loop. The AI never acts unilaterally at the boundary of your risk tolerance.
Every action, every decision point, every finding — logged with timestamp, technique reference, scope confirmation, and authorization chain. Generated automatically. Ready for your auditor the day they ask, not the week after.
Built under DARPA. Validated by DoD.
SIEGE's adversarial methodology was developed under the DARPA CASTLE program and validated through Department of Defense exercises. The same engine that tests defense networks now tests whether your AI controls would hold.
SIEGE — your adversarial seal of approval.
Briefs and checklists for AI governance leaders.
Questions teams ask before a SIEGE engagement.
Is SIEGE a shadow-AI detection tool?
Which frameworks does SIEGE map to?
Will adversarial testing disrupt our AI systems?
How fast can we get to first evidence?
The regulators have stopped asking whether you use AI.
They're asking whether you can prove it's governed. SIEGE is the only platform that answers that question with DoD-validated, adversarial proof, not paperwork. Start with a 15-minute assessment.