Deterministic
AI Governance
Probabilistic guardrails tell you an action is probably fine. Regulated industries can't run on "probably." Deterministic governance enforces policy with reproducible logic and leaves a record an examiner can verify — the same verdict, every time, with proof of why.
EU AI Act · SR 11-7 · HIPAA · ECOA · NIST AI RMF
same out
The case for deterministic governance
Each topic has its own page — start anywhere.
What Deterministic Means →
Rule-based, reproducible, accountable enforcement — a control, not a suggestion.
Why Probabilistic Fails →
The questions an examiner asks that a classifier score can't answer.
Regulatory Mappings →
EU AI Act, SR 11-7, HIPAA, ECOA, NIST AI RMF — obligation to control.
Governance Principles →
The five commitments every EVE product is built to satisfy.
Articles & Research →
Long-form thinking on enforceable, provable AI governance.
Academy & Research →
Training as signed evidence, plus the patent portfolio behind it.
Governance you can import
Enforce policy and independently verify the resulting evidence from your own code. Published on PyPI and npm.
# pip install eve-coreguard from eve_coreguard import CoreGuardClient client = CoreGuardClient(api_key="eve_sk_…") result = client.evaluate( tenant_id="org_acme", proposed_action={"type": "disclose_phi"}, policy_set="hipaa_v1", ) print(result.verdict) # ALLOWED | BLOCKED | MODIFIED
# pip install eve-coreguard[verify] (or: eve-governance) from eve_coreguard import CoreGuardClient # confirm a signed decision yourself — no network, no vendor ok = CoreGuardClient.verify_proof(proof.raw, signing_key=key_hex) print("✓ authentic" if ok else "✗ tampered")
Book a Governance Assessment
A working session mapping your highest-risk AI workflows to deterministic controls and the evidence your examiners will ask for.