Zytra AI Governance gives BFSI institutions a living risk atlas, measured control effectiveness, and board-ready residual scoring — so the answer to "how exposed are we?" is a number, not a conversation.
MRM asks: does this model perform as specified on validation data? It does not ask what happens when a RAG pipeline retrieves the right document and still outputs the wrong number — or when an agent acquires capabilities beyond its declared scope.
Knowing which models are in production and when they were last validated tells you nothing about hallucination rates, prompt injection exposure, or fairness gaps in a BFSI context. Those require a separate framework.
A control listed in a GRC tool is not a tested control. Design-stage controls that have never been proven to work carry 0% effectiveness — even when they appear in an audit submission as "implemented."
The board's question is: after our controls are applied, how exposed are we? A list of 141 risks is not an answer. A domain heat map with residual scores is. Most institutions cannot produce one.
The atlas is the foundation. 77 agentic and 64 foundational AI risks, each classified by domain, mapped to regulatory frameworks at the clause level, tagged to deployment patterns, linked to controls, and scored for residual exposure after those controls are applied.
Structured catalogue across Model Accuracy, Security, Fairness, Governance, Privacy, Agent Autonomy, Multi-Agent Security, Tool Risk, and more — organized by domain, not by product or model name, so cross-cutting risks don't fall through the gaps.
Each risk mapped to NIST AI RMF, OWASP LLM Top-10, MITRE ATLAS, ISO/IEC 42001, EU AI Act, DORA, and RBI guidelines — at the clause level, not just the framework name. Audit translation work done before the auditor arrives.
Each risk tagged to RAG pipelines, autonomous agents, multi-agent orchestration, document summarization, or classification. Filter the atlas to "risks relevant to my mortgage extraction pipeline" — not a reference document, a deployment checklist.
Each risk linked to specific controls with named owners, implementation status, test evidence, and last-validated date. A control without an owner is a recommendation. A control with a test result and a timestamp is a governance fact.
Every risk graded as Realized (observed in production), Demonstrated (proven in research), or Theoretical (plausible, unobserved) — so risk management effort is proportional to actual threat evidence, not just regulatory prominence.
After controls are applied, what exposure remains? Residual risk score = inherent risk × (1 − control effectiveness). The output is a board-ready heat map: which domains are still at High, and where the next investment has the most impact.
You don't need to reach Level 5 before deploying your first AI system. But you need to know which level you occupy — because the gap between current maturity and deployment ambition is itself a risk.
Risk knowledge lives in individual engineers. No formal catalogue. Incidents discovered after the fact. Coverage is a function of who happens to know which system.
Risks have been named. A list exists, probably in a spreadsheet. No consistent domain classification, no framework mapping, no controls assigned. Created once, not updated since.
Risks classified by domain, mapped to regulatory frameworks, linked to controls with assigned owners. The atlas is reviewable by an auditor. This is where Zytra starts.
Controls have effectiveness scores. Residual risk is quantified per domain. The atlas answers: "After our controls, what is our actual exposure?" Heat maps drive investment decisions.
The atlas is a living system. Board-level risk appetite defined against specific domains. Monitoring automated where possible. Regulator inquiries answered with evidence, not policy documents.
Four dimensions determine whether a control actually reduces risk — or just appears to. Zytra measures each one separately before combining them into a residual score.
Does the control address the risk comprehensively, or only partially? A hallucination guardrail active in pre-production but not in the production serving path has partial coverage — and partial coverage at the point of exposure is no coverage at all.
How much does the control actually reduce risk when present? A detection-only control (logging, alerting) has lower effectiveness than one that prevents or blocks. Effectiveness requires empirical testing — it cannot be assumed from design specifications.
Can you produce evidence that the control works? A policy document is a control. An automated pipeline test that runs daily against adversarial inputs is also a control. They are not equivalent. Testable controls produce audit-ready evidence. Untestable controls produce an argument.
Does the control satisfy the specific clause of the applicable framework? Clause-level alignment is verifiable. Framework-level alignment is marketing. The distinction matters when a regulator asks you to cite the exact provision your control satisfies.
The output the board needs is not a catalogue. It's a signal: where is residual exposure still High after controls, and where has it been adequately reduced?
Illustrative residual scores. Your heat map reflects your deployment patterns, control portfolio, and inherent risk assessment — not a generic template.
Inventory every AI system in production — models, agents, RAG pipelines, document workflows. Tag each to its deployment pattern. Zytra maps each deployment to the relevant risk domains and applicable regulatory frameworks automatically.
For each risk in scope, score impact (financial, reputational, regulatory, operational) and likelihood (deployment frequency, capability maturity, threat actor exposure). Inherent risk = impact × likelihood, scored 1–16 across a 4×4 matrix.
Assign controls with named owners, test evidence, and clause-level regulatory alignment. Score each control across coverage, effectiveness, testability, and regulatory alignment. Design-stage controls with no test evidence score 0% — that's the honest starting position.
Residual risk = inherent × (1 − effectiveness). The output is a domain heat map showing where exposure remains High after controls, and a gap-analysis backlog ranked by residual impact. Your regulator submission cites evidence — not a policy document.
Clause-level crosswalks — not just framework names — so audit translation is done before the auditor arrives.
AI risk taxonomy aligned to RBI's operational risk and model risk management circulars and SEBI's algorithm oversight guidelines. Control evidence formatted for Indian regulatory submissions.
Privacy-related AI risks mapped to the Digital Personal Data Protection Act, including data minimisation, consent management, and erasure obligations for AI training and inference pipelines.
High-risk AI system obligations (Art. 9–15), GPAI requirements (Art. 53–55), and conformity assessment pathways — pre-mapped so the governance team isn't starting from scratch at the deadline.
Full crosswalk across GOVERN, MAP, MEASURE, and MANAGE functions, with specific sub-practice citations for every risk entry. The GenAI profile covered in full.
LLM Top-10 and Agentic Top-10 mapped to the Zytra risk taxonomy, with MITRE ATLAS technique IDs for adversarial ML risks. Security team and risk team speaking the same language.
AI management system requirements (42001) and ICT risk management obligations (DORA) pre-mapped so the governance framework satisfies both simultaneously, without duplication.
Every bank that has experienced an AI-related incident has found, in the post-mortem, a failure mode that existed in the literature but was not in their catalogue. Zytra AI Governance is the catalogue — and the measurement system on top of it.