SustainGRC AI Governance
The gap isn't awareness. It's architecture. One system that discovers, classifies, and continuously governs every AI system in your organisation — against EU AI Act, ISO 42001, and NIST AI RMF. From one record.
The real risk isn't the AI systems organisations know about. It's the ones they don't. Shadow AI, unregistered models, tools adopted at team level without governance sign-off — that's where the liability sits.
Fewer than half can demonstrate measurable maturity. Not because they lack intent — because they lack infrastructure.
Shadow AI is the compliance blind spot quarterly audit cycles can't catch. The exposure is embedded before risk even knows to look.
Or 7% of global annual turnover — whichever is higher. The window between "we have a policy" and "we have a finding" is closing fast.
How it works
From shadow AI discovery to board reporting — every step produces auditable evidence, not documentation.
Shadow AI is where the liability sits. SustainGRC passively discovers AI API calls across your network, flags AI-enabled vendors at procurement, and provides self-service intake for business units. Every discovery enters the registry with mandatory owner assignment before it progresses
Network scan for known AI providers (OpenAI, Azure AI, Google Vertex, AWS Bedrock)
Procurement hook flags AI-enabled vendors automatically
Self-service intake for business-unit-led registration

Security, transparency, and auditability are foundational—not afterthoughts.
High-risk AI systems automatically create linked entries in Enterprise Risk Management. AI-specific controls slot into your existing Controls Library. No parallel silo.
Every classification and readiness score shows its formula, inputs, and regulatory basis. No black-box scoring. No maturity models. Binary: compliant or finding raised.
AI assists with classification and gap detection. It never computes final scores or makes governance decisions. Every suggestion requires explicit human confirmation.
AI governance data feeds directly into CSRD disclosures (ESRS G1, S1), materiality assessments, and climate model governance — because sustainability reports must now cover AI risk.
How it works
If a feature requires a data scientist to configure or a PhD to interpret, it doesn't belong in a governance tool.
We surface alerts from your existing tools. We don't rebuild Datadog.
Bias testing is domain-specific. We ingest results as evidence. We don't score fairness.
Subjective, non-auditable, easily gamed. We do rule-based gap analysis against published standards.
LIME/SHAP dashboards don't answer the CISO's question. Art. 13 demands plain-language purpose statements.
AI Governance data flows across your enterprise GRC and sustainability reporting — because every AI finding already maps to risk, controls, and disclosure.
High-risk AI systems auto-create linked risk entries with inherited controls.
AI system data feeds ESRS G1 governance and S1 workforce impact disclosures.
Pre-built one-page view: risk tiers, finding severity, compliance trajectory.
AI-specific controls slot into existing taxonomy. Same audit framework.
'Responsible AI' backed by registry evidence — not opinion surveys.
AI-enabled vendors flagged at procurement trigger automatic registry intake.
Regulation isn't waiting. Prohibited AI practices and GPAI transparency rules are already in force. High-risk enforcement is next.
February 2025
Social scoring, manipulative AI, real-time biometric surveillance (with exceptions).
August 2025
General-purpose AI model providers must comply with transparency and copyright obligations.
2 August 2026
Conformity assessments, risk management, technical documentation, human oversight, and post-market monitoring become mandatory.
August 2027
All remaining provisions including AI embedded in regulated products.
Discovery to board reporting in weeks, not quarters. No data science team required.