Governance Capability Brief
AI Governance Assessment
AI governance is cross-cutting - it applies regardless of which technical testing categories an organization needs. We assess the maturity of AI security programs, evaluate whether controls hold under real conditions, and help organizations close the gap between documentation and actual security posture.
What we assess
Five dimensions of AI governance
Development Pipeline Gates
Are there security and RAI review checkpoints built into the AI development lifecycle, and are they actually enforced? We evaluate whether safety testing happens before deployment or gets skipped for urgent releases.
Responsible AI Framework Implementation
The gap between policy documents and engineering reality. We assess whether RAI commitments are translated into actual technical controls (classifiers, guardrails, instruction hierarchies, human confirmation gates) or just exist on paper.
Agent and Automation Governance
For organizations deploying AI agents, we evaluate the governance controls around autonomous capabilities: sandbox policies, tool access controls, breakpoint requirements, and whether these hold under adversarial conditions.
Data Handling and Privacy
Data classification practices, RAG pipeline integrity, PII handling, memory management, and whether data flows between components maintain appropriate boundaries.
Compliance Alignment
Practical alignment with NIST AI RMF, ISO/IEC 42001, EU AI Act, and industry-specific regulations. Not just whether policies exist, but whether implementation would withstand audit scrutiny.
How we approach it
We don't just read policies - we test whether they work
Our governance assessments combine document review, stakeholder interviews, and hands-on technical evaluation. We don't just read policies - we test whether they work.
We build custom test harnesses to probe agent governance controls at scale. We work closely with product teams, security teams, and compliance stakeholders. Our recommendations are practical and specific - not generic framework checklists.
What we find
Typical finding patterns
Safety Controls That Don't Compose Well
Individual defenses work in isolation but interactions between them create gaps.
Pipeline Gates That Aren't Enforced
Documented review processes where gates aren't consistently applied before deployment.
Incomplete Threat Modeling for AI-Specific Risks
Traditional threat models that don't capture prompt injection, training data poisoning, or AI agent trust boundaries.
Governance Documentation That Doesn't Reflect Operational Reality
Policy documents describing controls that aren't implemented or are implemented differently than documented.
Why this matters
Not a checkbox exercise
AI governance is not a checkbox exercise. Organizations deploying AI systems need governance programs grounded in technical reality and tested against real threats. The gap between policy documentation and actual security posture is where risk lives.
Need help with your AI governance program?
We'll help you build one that holds up under scrutiny - not just on paper.
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