CoF Audit — Deterministic Verification Engine
CoF Audit is a deterministic verification engine that sits between AI models and their users. Every AI response is checked against structured safety rules before it reaches the recipient. The result is binary: approved or blocked, with a complete, auditable decision trail. Model-agnostic, integrates as a proxy via a single line of code.
The gap between policy and enforcement
AI has moved from experiment to operational infrastructure. Models produce responses that directly affect clinical decisions, financial advice, customer interactions, and internal processes. Organizations have policies for how AI should be used, but rarely technical means to verify per individual response that the policies are followed.
Existing tools solve partial problems. Guardrails libraries require manual rule definition in code and offer no audit infrastructure. AI-on-AI solutions (one model judging another) inherit the same probabilistic uncertainty they are supposed to control. GRC platforms handle documentation and ownership but lack runtime verification per AI response. Observability tools log traffic after the fact but perform no pre-action verification.
The gap CoF Audit closes: the difference between documented policy and operationally verified compliance, per response, with evidence that holds up to audit.
Verify, don't reason
Like an aircraft's flight control system: pre-flight checklist before every response, envelope protection for safety limits, flight data recorder for every decision. AI-on-AI is not the solution. Deterministic verification is.
Deterministic rule engine
Every verification produces an identical result every time, byte-identical across runs. No ML, no randomness, no external dependencies in the verification step. The decision trail is structured, auditable, and holds up to regulatory scrutiny.
Cryptographic audit trail
Every decision is backed by a cryptographic hash chain. Input, decision, constraints, metrics, findings, versions, and policy are all captured in a tamper-proof record. Not just a log, a structured decision artifact.
Coverage visibility
Every verification shows not only what was checked, but also what was not checked. No false sense of security. You get an honest picture of where you stand, including the gaps.
Model-agnostic
Works with any AI system: leading cloud providers, open-source models, or proprietary systems. Switch models without reconfiguring verification. The verification layer is independent of the AI it verifies.
Healthcare example domain
Healthcare was chosen as example domain because it is one of the most safety-critical applications, where the value of deterministic verification is most visible. Clinically dangerous failure modes have been identified and documented, including cross-reactivity errors, outdated guideline references, and missed drug interaction risks.
Vertical-agnostic architecture
The verification architecture is not tied to healthcare. It applies to any domain where AI output needs deterministic verification: financial compliance, legal AI, insurance, customer service, autonomous systems.
Designed for EU AI Act compliance
EU AI Act drives demand, particularly in high-risk environments, but the value exists regardless of regulation: AI that affects customers, patients, and decision-makers needs deterministic verification. CoF Audit is a critical building block for compliance, not a total solution.
| Requirement | EU AI Act | CoF Audit |
|---|---|---|
| Risk management | Art. 9 | Safety rules + audit trail |
| Record-keeping | Art. 12 | Cryptographic hash chain per decision |
| Human oversight | Art. 14 | Governed mode with category-level approval |
| Robustness | Art. 15 | Deterministic, byte-identical verification |
| Transparency | Art. 13 | Operator + regulator views per audit |
Also addresses technical requirements in DORA (financial sector), NIS2 (critical infrastructure), and MDR (medical devices).
From verification engine to policy operating system
Today: Deterministic engine
Structured safety rules, byte-identical verification, cryptographic audit trails, coverage visibility. Model-agnostic proxy integration. Healthcare example domain with verified clinical scenarios and structured data integration.
Next: Adaptive intelligence
Automatic risk discovery from AI traffic, so organizations don't need to write rules manually. Graduated activation where every rule earns its authority through proven performance: observe, then soft block, then hard block. Reasoning chain verification that catches right-answer-wrong-reasoning failures. Fabrication detection integrated as an additional verification signal. Dynamic verification depth per response based on complexity and domain.
Vision: Policy Operating System
Self-improving rule libraries where new patterns are discovered and underperforming rules degraded automatically. Cross-vertical contract templates. Model-analysis-generated rules from MCG. The technical primitive that makes AI policy executable, not just documented.
CoF Audit — Common questions
What is deterministic AI verification?
Every AI output is checked against predefined safety rules, producing an identical result every time, byte-identical across runs. No randomness, no ML, no LLM in the verification step. The result holds up to audit.
Why not use AI to check AI?
AI-on-AI solutions inherit the same probabilistic uncertainty they are supposed to control. You cannot prove to a regulator that your verification is reliable when the verifier itself is unreliable. Deterministic verification produces identical results every time, which is what compliance requires.