AI Security GRC in an Era of Expanding Regulatory Obligations

As AI adoption accelerates, organizations face growing pressure to demonstrate that AI systems are not only effective but also secure, compliant, and auditable. Regulators across the EU, Gulf, and global markets are embedding AI-specific obligations into existing GRC frameworks, requiring organizations to integrate AI risk into enterprise risk management, align AI controls with ISO/IEC 42001 and NIST AI RMF, and produce audit-ready evidence of compliance. Without a structured AI Security GRC programme, organizations risk regulatory penalties, failed audits, and reputational harm from AI systems that operate outside any formal control environment.

Hangul’s Approach to AI Security GRC

Hangul’s AI Security GRC practice integrates AI-specific security controls into existing governance, risk, and compliance frameworks. We work across policy design, control mapping, regulatory alignment, and audit readiness to ensure organizations can demonstrate that AI systems are managed, monitored, and controlled. Whether building an AI GRC programme from the ground up, integrating AI risk into an existing enterprise GRC function, or preparing for regulatory examination, Hangul provides the structure, expertise, and documentation to do it effectively.

Comprehensive AI Security GRC Services

Hangul delivers integrated AI Security GRC capabilities designed to embed AI risk controls into enterprise governance frameworks, align AI programmes with applicable regulatory requirements, and build the audit evidence that regulators and boards require.

1

AI GRC Framework
Design

Designing governance, risk, and compliance structures tailored to AI.

  • AI-specific GRC policy design, ownership structures, and accountability frameworks
  • Mapping AI controls to existing enterprise GRC and risk management frameworks
  • AI risk appetite definition and control thresholds aligned to board risk tolerance
  • Integration of AI risk into enterprise risk registers and reporting cycles

2

AI Regulatory Compliance
Programme

Aligning AI programmes with applicable regulatory frameworks and obligations.
  • Gap assessment against EU AI Act, UAE AI Policy, Saudi SDAIA, and ISO/IEC 42001
  • Regulatory mapping of AI use cases to applicable obligations and risk tiers
  • Internal AI compliance policy development and board approval processes
  • Regulatory change monitoring and compliance calendar management

3

AI Risk & Control Framework

Building risk-based controls across the AI model lifecycle.
  • AI-specific control design aligned to model development, deployment, and operation
  • Three lines of defence mapping for AI risk across business, risk, and audit functions
  • Key Risk Indicators (KRIs) and Key Control Indicators (KCIs) for AI systems
  • AI incident management, escalation frameworks, and suspension protocols

4

AI Audit Readiness & Evidence Management

Building the documentation and audit evidence that regulators and auditors require.
  • Model cards, system documentation, and technical evidence libraries for deployed AI
  • Control testing, risk assessment records, and decision log maintenance
  • Audit-ready documentation packages structured for internal audit and regulatory review
  • AI GRC maturity reporting for leadership, risk committees, and boards

5

Third-Party AI Risk Management

Extending GRC controls to cover AI systems and tools sourced from external providers.
  • Third-party AI vendor risk assessment framework and due diligence processes
  • AI-specific contract clauses and supplier assurance requirements
  • Ongoing third-party AI monitoring and periodic re-assessment cadences
  • Escalation and exit processes for high-risk AI supplier relationships

6

AI GRC Monitoring & Reporting

Sustaining ongoing visibility of AI risk across the organization through structured reporting.
  • Continuous AI risk monitoring frameworks and automated control tracking
  • Periodic AI GRC maturity assessments and control effectiveness reviews
  • Board and executive AI risk dashboards with KRI and KCI reporting
  • Regulatory reporting support and audit liaison for AI-related examinations
AI GRC Framework Design

Designing governance, risk, and compliance structures tailored to AI.

  • AI-specific GRC policy design, ownership structures, and accountability frameworks
  • Mapping AI controls to existing enterprise GRC and risk management frameworks
  • AI risk appetite definition and control thresholds aligned to board risk tolerance
  • Integration of AI risk into enterprise risk registers and reporting cycles
AI Regulatory Compliance Programme

Aligning AI programmes with applicable regulatory frameworks and obligations.

  • Gap assessment against EU AI Act, UAE AI Policy, Saudi SDAIA, and ISO/IEC 42001
  • Regulatory mapping of AI use cases to applicable obligations and risk tiers
  • Internal AI compliance policy development and board approval processes
  • Regulatory change monitoring and compliance calendar management
AI Risk & Control Framework

Building risk-based controls across the AI model lifecycle.

  • AI-specific control design aligned to model development, deployment, and operation
  • Three lines of defence mapping for AI risk across business, risk, and audit functions
  • Key Risk Indicators (KRIs) and Key Control Indicators (KCIs) for AI systems
  • AI incident management, escalation frameworks, and suspension protocols
AI Audit Readiness & Evidence Management

Building the documentation and audit evidence that regulators and auditors require.

  • Model cards, system documentation, and technical evidence libraries for deployed AI
  • Control testing, risk assessment records, and decision log maintenance
  • Audit-ready documentation packages structured for internal audit and regulatory review
  • AI GRC maturity reporting for leadership, risk committees, and boards
Third-Party AI Risk Management

Extending GRC controls to cover AI systems and tools sourced from external providers.

  • Third-party AI vendor risk assessment framework and due diligence processes
  • AI-specific contract clauses and supplier assurance requirements
  • Ongoing third-party AI monitoring and periodic re-assessment cadences
  • Escalation and exit processes for high-risk AI supplier relationships
AI GRC Monitoring & Reporting

Sustaining ongoing visibility of AI risk across the organization through structured reporting.

  • Continuous AI risk monitoring frameworks and automated control tracking
  • Periodic AI GRC maturity assessments and control effectiveness reviews
  • Board and executive AI risk dashboards with KRI and KCI reporting
  • Regulatory reporting support and audit liaison for AI-related examinations

What Effective AI Security GRC Delivers

Integrated AI Risk

AI risk embedded in enterprise GRC, with clear ownership, controls, and reporting aligned to organizational risk appetite.

Regulatory Readiness

Compliance programmes aligned to the EU AI Act, ISO/IEC 42001, UAE AI Policy, and Gulf frameworks before enforcement arrives.

Audit-Ready Evidence

Documentation, control records, and decision logs that satisfy internal audit, external regulators, and board governance requirements.

Scalable GRC Infrastructure

GRC frameworks designed to scale as AI programmes expand, supporting responsible adoption within a structured, controlled environment

A Structured Path from AI GRC Gap to Governed,
Audit-Ready AI Risk Programme

Assess AI GRC Maturity & Gaps

We begin with a structured assessment of existing GRC frameworks, AI risk posture, and regulatory exposure to identify gaps and prioritize remediation.

  • Current-state review of AI governance, risk, and compliance policies and controls
  • Regulatory gap analysis across applicable AI frameworks (EU AI Act, ISO/IEC 42001, UAE AI Policy)
  • AI risk register review and enterprise GRC integration assessment
  • Control coverage analysis across the AI model lifecycle
  • Stakeholder interviews across risk, compliance, legal, IT, and business teams

Design the AI GRC Framework

Based on assessment findings, we design an AI GRC framework that integrates with existing enterprise risk management infrastructure and meets applicable regulatory requirements.

  • AI GRC policy design, ownership structures, and governance accountability framework
  • AI control framework mapped to regulatory requirements and organizational risk appetite
  • Third-party AI risk management framework and supplier assurance processes
  • AI audit evidence standards, documentation templates, and control testing protocols
  • Integration design for AI risk reporting into board and executive governance cycles

Implement Controls & Compliance Processes

Hangul implements the AI GRC framework across policies, controls, reporting, and documentation, embedding AI risk into existing governance and risk management processes.

  • Policy rollout, training, and stakeholder communication across risk, compliance, and business teams
  • Control implementation across the AI model lifecycle from development through operation
  • AI regulatory compliance programme activation and regulatory filing support where required
  • Audit evidence library build and documentation package preparation for regulatory review
  • Integration of AI risk reporting into enterprise GRC platforms and board reporting structures

Sustain AI GRC as Programmes and Regulations Evolve

The AI regulatory environment continues to evolve, and AI programmes expand. Hangul provides ongoing support to keep GRC frameworks current, effective, and audit-ready.

  • Continuous AI control monitoring and KRI/KCI reporting
  • Regulatory change management and policy update processes as new obligations emerge
  • Periodic AI GRC maturity reviews and control effectiveness assessments
  • Third-party AI risk re-assessment and supplier monitoring cadences
  • Board and executive AI risk reporting and regulatory examination support

Integrate AI Risk Into Your Governance Framework Before Regulators Require It

Connect with Hangul to assess your current AI GRC maturity, identify the control and documentation gaps that carry the greatest regulatory exposure, and design a framework that holds up to internal audit, board scrutiny, and regulatory review.

Talk to an Expert

FAQs

What is AI Security GRC and how does it differ from general GRC?
What is ISO/IEC 42001 and what does it require organizations to do?
What documentation and evidence do regulators and auditors require for AI compliance?
How does the three lines of defence model apply to AI risk management?
How should organizations manage AI risk from third-party AI vendors and tools?
What does an AI Security GRC engagement typically involve and how long does it take?
AI Security GRC embeds AI-specific governance, risk, and compliance controls into an organization’s enterprise risk framework. Unlike general GRC, it addresses risks unique to AI systems — model bias, explainability, auditability, and regulatory obligations under frameworks such as the EU AI Act and ISO/IEC 42001 — which standard control frameworks were not designed to manage.
ISO/IEC 42001 is the international standard for AI management systems, published in 2023. It requires organizations to define their AI program scope, assess AI-related risks, implement appropriate controls, and maintain documented compliance evidence — including governance accountability structures, impact assessments, and a continuous improvement process for AI systems across their lifecycle.
AI regulators and auditors require four categories of evidence: system documentation including model cards; risk assessments conducted before deployment; decision and control logs showing governance processes were followed; and ongoing monitoring records demonstrating that deployed AI systems are tracked for performance, drift, and compliance on a continuing basis — not only at deployment.
Applied to AI risk, the first line — development and operations teams — implements AI controls and escalates issues. The second line — risk and compliance — designs the control framework and provides independent oversight. The third line — internal audit — independently assesses whether AI governance controls are designed appropriately and operating effectively.
Third-party AI risk requires AI-specific due diligence covering model governance, training data provenance, bias testing, and regulatory compliance — beyond standard vendor security questionnaires. Contracts should include transparency, audit rights, and incident notification clauses. Ongoing monitoring is required because AI systems change through model updates in ways a static assessment will not capture.
An AI Security GRC engagement proceeds in three phases: assessment covering regulatory gap analysis and control review, taking four to six weeks; framework design and implementation covering policy, controls, and documentation standards, spanning ten to sixteen weeks; and ongoing monitoring and regulatory change management operating continuously once the framework is in place.

FAQs

AI Security GRC embeds AI-specific governance, risk, and compliance controls into an organization’s enterprise risk framework. Unlike general GRC, it addresses risks unique to AI systems — model bias, explainability, auditability, and regulatory obligations under frameworks such as the EU AI Act and ISO/IEC 42001 — which standard control frameworks were not designed to manage.
ISO/IEC 42001 is the international standard for AI management systems, published in 2023. It requires organizations to define their AI program scope, assess AI-related risks, implement appropriate controls, and maintain documented compliance evidence — including governance accountability structures, impact assessments, and a continuous improvement process for AI systems across their lifecycle.

AI regulators and auditors require four categories of evidence: system documentation including model cards; risk assessments conducted before deployment; decision and control logs showing governance processes were followed; and ongoing monitoring records demonstrating that deployed AI systems are tracked for performance, drift, and compliance on a continuing basis — not only at deployment.

Applied to AI risk, the first line — development and operations teams — implements AI controls and escalates issues. The second line — risk and compliance — designs the control framework and provides independent oversight. The third line — internal audit — independently assesses whether AI governance controls are designed appropriately and operating effectively.

Third-party AI risk requires AI-specific due diligence covering model governance, training data provenance, bias testing, and regulatory compliance — beyond standard vendor security questionnaires. Contracts should include transparency, audit rights, and incident notification clauses. Ongoing monitoring is required because AI systems change through model updates in ways a static assessment will not capture.

An AI Security GRC engagement proceeds in three phases: assessment covering regulatory gap analysis and control review, taking four to six weeks; framework design and implementation covering policy, controls, and documentation standards, spanning ten to sixteen weeks; and ongoing monitoring and regulatory change management operating continuously once the framework is in place.

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