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Your AI policy is written. Nothing enforces it.
Compliance owns the policy, the training, the attestations, and none of it touches the moment an agent acts. Nexovern is the mechanism that turns written policy into enforced controls, with the evidence trail your next audit will ask for.
The enforcement vacuum
We have an AI ethics policy and model documentation. We have no mechanism at the moment of decision.
The mechanism problemFewer than a quarter of boards have approved structured AI policies, and even approved policies typically stop at the document. The Replit incident happened inside a declared code freeze.
→ Manage compiles policy into controls that act at the moment of the action: the approval requirement, the data boundary, the block on sensitive data leaving for an AI vendor, enforced, not requested.
Our lending AI can't produce the specific adverse-action reasons the law requires.
The explainability problemECOA/Reg B demand specific, accurate reasons; the CFPB is explicit that there is no advanced-technology exception to federal consumer law.
→ Measure captures the full AI session behind every automated outcome, so each decision has a reconstructable basis.
DPDP gives me 72 hours to report a breach. I'd need 72 days to figure out what the agent touched.
The evidence-clock problemDPDP Rules require 72-hour breach reporting to the Board; Significant Data Fiduciaries face annual audits and DPIAs; penalties reach ₹250 crore per violation.
→ Agent-attributed data-access records mean the "what was touched" question is a query, not an investigation.
Every audit becomes a scramble to assemble evidence that should already exist.
The audit-readiness problemRBI FREE-AI expects audit processes and incident reporting; ISO 42001 expects operating evidence; SEBI expects execution logs. Manual evidence assembly doesn't scale.
→ Continuous, exportable compliance documentation structured for the frameworks you answer to.
Compliance that operates, not just documents
- Author your own policies write the rules in your language, change them any time, and they take effect at the endpoint and the gateway at once.
- Decision evidence adverse actions, claim outcomes and every data access reconstructable on demand from the AI session.
- Sensitive-data control PII and secrets detected in AI sessions and blocked before they reach an external vendor.
- Always-on forensic log the examination binder that writes itself.
Your first 30 days
The engagement is structured to produce a defensible result at each stage, starting with the question every framework asks first: what do you actually have running?
Days 0–5 · Discover
Runtime discovery across your estate. Output: a complete, risk-classified inventory of every agent in production, including the ones nobody registered.
Days 6–15 · Evidence
System-level telemetry live on your priority agents. Output: your first full incident-grade reconstruction, plus a gap report against the frameworks you answer to.
Days 16–30 · Enforce
Your highest-priority policies compiled into runtime gates, approval checkpoints, blast-radius limits, kill switches, with assurance reporting flowing to your committee.
Walk into the next audit already ready.
Bring your AI policy to a demo. We'll show you which clauses can be enforced at runtime, and what the evidence trail looks like.