The risk you haven't named is a risk you cannot govern.
Your model risk framework was designed for statistical models. It was not designed for RAG pipelines that hallucinate, agents that acquire capabilities beyond their declared scope, or LLMs that give customers incorrect regulatory deadlines. Zytra AI Governance extends MRM to generative AI — a living risk atlas, measured control effectiveness across four dimensions, and board-ready residual scoring. When your OCC examiner or RBI inspector asks how exposed you are, the answer is a number, not a conversation.
RBI's 2024 AI circular mandates evidence trails for every production AI system. EU AI Act non-compliance: €35M or 7% of global turnover. SR 11-7 MRM committees require validation evidence your team can't produce manually. Zytra delivers the evidence chain before the examination arrives.
Your model risk register tracks validation status. It doesn't govern what happens in production.
SR 11-7 MRM asks: does this model perform as specified on its validation data? It does not ask what happens when a RAG pipeline retrieves the right document and outputs the wrong number, when a credit decisioning agent acquires capabilities beyond its approved scope, or when an AML alert system hallucinates a transaction pattern. Those are the failure modes your next regulatory examination will surface.
Model inventory ≠ risk atlas
Knowing which models are in production and when they were last validated tells you nothing about hallucination rates in credit summaries, prompt injection exposure in customer assistants, or fairness gaps in lending decisions. SR 11-7 was written for statistical models. Generative AI requires a broader framework — and so does your regulator.
Controls without measurement
A control listed in your 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 OCC examination submission as "implemented." The examiner's follow-up question — "can you show me the test evidence?" — is the one most institutions cannot answer.
No residual score for the board
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.
141 AI risks. 77 agentic, 64 foundational. Every framework a BFSI regulator will ask about.
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.
Risk taxonomy
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.
Framework crosswalks
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.
Deployment-pattern tagging
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.
Control assignments
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.
Evidence levels
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.
Residual scoring
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.
Five levels. Most banks sit at Level 2.
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.
Reactive
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.
Catalogued
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.
Mapped
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.
Measured
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.
Governed
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.
From SR 11-7 to RBI Circular — one workflow engine
Model Risk Management wasn't designed for generative AI. SR 11-7 established the framework for statistical models; it didn't anticipate agents that chain tool calls, RAG pipelines that hallucinate, or LLMs that produce inconsistent outputs under adversarial conditions. Zytra extends MRM to cover these failure modes — giving all three lines of defense a shared evidence base, without replacing the validation framework your first line already owns.
Inventory & Risk Tiering
Auto-classify every AI system by risk tier — Critical / High / Medium / Low / Minimal. Continuous re-assessment, not point-in-time snapshots.
Validation & Issue Tracking
Track validation status, issue resolution, and approvals in one audit trail. Export for SR 11-7 MRM committees in the format they expect.
Audit-Trail Export
PDF, OSCAL, and regulator-ready audit packs. Every evidence item is hash-chained and timestamped. Export on demand, not the night before an inspection.
Signed Evidence Ledger
Committee approvals and validation sign-offs written to a tamper-evident ledger. Board-ready reporting in one click, with a chain of custody your auditor can verify.
Foundation: Inventory, risk tiering, approvals. · Professional: + validation, issue tracking, OSCAL export. · Enterprise: + custom workflows, committee approvals, signed evidence ledger.
A control listed in your GRC tool is not a tested control.
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.
Coverage
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.
Effectiveness
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.
Testability
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.
Regulatory alignment
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.
Control Effectiveness = avg(Coverage, Testability, Effectiveness, Regulatory Alignment) · scaled 0–1
Example: Critical risk (score 12) with 40% control effectiveness → residual score 7.2 (still High). Reaching Low requires either reducing inherent risk or achieving >85% combined effectiveness.
A domain heat map, not a list of 141 risks.
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.
From catalogue to board report in four steps.
Map your AI portfolio
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.
Assess inherent risk
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.
Measure control effectiveness
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.
Generate residual scores and the board report
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.
Mapped to every framework your regulator will ask about.
Clause-level crosswalks, not just framework names, so audit translation is done before the auditor arrives.
RBI & SEBI
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.
DPDP Act 2023
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.
EU AI Act
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.
NIST AI RMF
Full crosswalk across GOVERN, MAP, MEASURE, and MANAGE functions, with specific sub-practice citations for every risk entry. The GenAI profile covered in full.
OWASP & MITRE ATLAS
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.
ISO/IEC 42001 & DORA
AI management system requirements (42001) and ICT risk management obligations (DORA) pre-mapped so the governance framework satisfies both simultaneously, without duplication.
MAPPED TO 13 FRAMEWORKS
RBI, SEBI, and DPDP Act — the only platform that covers all three
No AI governance platform other than Zytra maps to India's regulatory frameworks. RBI's 2024 AI Circular and FREE-AI Framework (26 recommendations), SEBI's algorithmic AI oversight, and DPDP Act 2023 — at clause level, with examination-ready evidence.
FREE-AI Framework: 26 recommendations mapped
Fairness, Reliability, Explainability, Ethics — clause-level controls for every recommendation.
Read →India regulatory coverage hub
RBI, SEBI, DPDP, IRDAI — all four India AI frameworks with sector-specific landing pages.
Explore →RBI AI Circular compliance checklist
What an RBI examiner will ask for — six obligation categories with evidence requirements.
Read →MeitY India AI governance guidelines — Seven Sutras, AIGG, TPEC, AI Safety Institute
India's national AI governance framework: Seven Sutras adapted from RBI FREE-AI, 26 recommendations across 6 pillars, three new institutions. National + sectoral layers converge on one governance program.
Read →Agentic AI governance India BFSI — Know Your Agent, HDFC Kotak SBI frameworks
Indian banks deploying agentic AI at pace. RBI MRMF applies — no SR 26-2 exclusion. SBI Know Your Agent (KYA): identity, authentication, consent, transaction limits, revocation.
Read →CERT-In AI cybersecurity blueprint — 12-hour mandate, AIBOM, adversarial testing
India's first AI-specific cybersecurity guidance: formal AI governance as security control, AIBOM, adversarial testing mandate. CERT-In + RBI MRMF satisfy from same governance layer.
Read →Answer your regulator before they ask the question.
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. Every institution that has faced a regulatory examination on AI has found, in the gaps, a control that was listed but not tested. Zytra AI Governance is the catalogue, the measurement system, and the evidence chain your examiner will accept.