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You're accountable for AI outcomes. Visibility didn't come with the job.
Under RBI FREE-AI and SEBI's frameworks, deployers answer for AI decisions regardless of autonomy. Yet most risk functions see AI through periodic reports, yesterday's view of a system that acts by the second. One CRO put it plainly: the role is becoming chief uncertainty officer.
The gap between accountability and sight
I sign off on AI risk with no real-time view of production behavior.
The visibility problemOnly 12% of risk leaders call their AI governance framework highly developed, while 54% run AI in production. Periodic assessment governs a continuous system.
→ Measure replaces the quarterly snapshot with a live view of what every AI session actually did, scored against policy as it happens.
Regulation holds us responsible "regardless of the level of autonomy."
The accountability problemRBI FREE-AI's accountability sutra and SEBI's deployer-liability stance both land outcomes on the institution, and on the risk office that certified the controls.
→ A defensible control framework: inventory, evidence, and enforcement mapped to the specific regulatory texts your examiner cites.
Tech and cyber risk is my top category, and agents are accelerating it.
The concentration problem74% of risk leaders cite technology and cyber as their top risk. Agents concentrate that risk: credentials, data access, and autonomous action in one identity.
→ Per-agent risk classification and blast-radius limits make agent risk quantifiable, and capped.
When the incident comes, I need the reconstruction in hours, not weeks.
The evidence problemThe scariest failures are the ones you can't reconstruct. Incident response without an action-level record is archaeology.
→ Push-button incident reconstruction: a complete ordered timeline of what the agent did, ready for the committee and the regulator.
Risk infrastructure for autonomous systems
- Continuous monitoring every AI session scored against policy in real time, replacing point-in-time assessment.
- Risk quantification every agent, MCP and endpoint classified by access, data reach and blast radius, with an org risk score.
- Regulatory mapping controls and evidence aligned to RBI, SEBI, OCC/Fed and CFPB expectations.
- Board reporting assurance that says "here is the evidence," not "we believe so."
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.
From "we believe so" to "here is the evidence."
A demo maps your AI risk framework against what your regulator now expects, and shows where runtime evidence closes the gap.