EU AI Act Compliance 2026
From AI innovation to AI governance — a practical reference for Compliance Officers, CIOs, CISOs, Internal Audit, SAP Security, IAM, and governance leaders preparing for EU AI Act enforcement in Central and Eastern Europe.
AI is now regulated infrastructure, not just technology.
Enforcement journey
The AI Act is phased. August 2026 is the primary enforcement pivot. The 2026 AI Omnibus extended certain high-risk categories to December 2027 and product-integrated systems to August 2028.
AI Act entered into force
Official Journal publication of Regulation 2024/1689. Phased application cycle begins with the full text establishing the four-tier risk model, prohibited practices, and penalty framework.
Prohibited practices & AI literacy
Article 5 bans on prohibited practices applicable. AI literacy obligations start — employers must ensure staff working with AI have the necessary competence and contextual understanding to interpret outputs.
GPAI model obligations
Governance rules for general-purpose AI model providers applicable. Foundation model operators must publish training data copyright summaries and comply with transparency obligations.
Primary enforcement milestone — governance pivot
Majority of rules, transparency obligations, high-risk system requirements, market surveillance, and enforcement mechanisms apply. Deployers of existing high-risk systems must also be compliant. The date all organisations must be audit-ready against.
Extended timelines — AI Omnibus
Following the 2026 AI Omnibus simplification agreement, certain Annex III high-risk categories shift to December 2027. AI systems embedded in regulated products shift to August 2028.
The four-tier risk model
Regulatory burden scales with risk to fundamental rights, safety, critical services, and consequential decisions. Classification determines all subsequent Art. 9–15 obligations.
Already illegal
Social scoring, manipulative exploitation, untargeted facial scraping, emotion recognition in workplaces and schools, real-time biometric surveillance in public (narrow law-enforcement exceptions only).
Strict obligations
Employment tools, critical infrastructure, credit scoring, essential services access, biometrics, migration, justice-related systems, education outcomes. All Annex III categories face Art. 9–15 compliance.
Disclosure duties
Chatbots must disclose AI nature. AI-generated content must be labelled. Deep fakes require disclosure. Users must be able to recognise AI interaction or AI-generated material at all times.
Low direct burden
Spam filters, AI-enabled games, most productivity AI. No specific AI Act duties — but documentation practice remains advisable for supply chain assurance and contractual protection.
What the law actually requires
For any high-risk AI system under Annex III, providers and deployers must meet eight mandatory requirement categories simultaneously and maintain documentary evidence of ongoing compliance.
| Article | Obligation | What it requires in practice | Deployer responsibility |
|---|---|---|---|
| Art. 9 | Risk management system | Continuous lifecycle process — not one-time assessment. Identify, analyse, estimate, evaluate, and mitigate foreseeable risks. Must be documented with defined review triggers. | Implement and document risk management procedure with escalation paths and periodic review cadence. |
| Art. 10 | Data & data governance | Training, validation, and testing data must meet quality criteria. Provenance documented. Bias evaluation performed. Data minimisation applied. Relevant demographic properties assessed. | Obtain and retain data governance documentation from providers. Verify claims. Include in procurement contracts. |
| Art. 11 | Technical documentation | Full system documentation before market placement: model card, system description, design specifications, training methodology, performance benchmarks, version history (Annex IV). | Require Annex IV-compliant documentation in vendor contracts. Verify completeness before any deployment. |
| Art. 12 | Automatic logging | Tamper-evident logs generated automatically. Minimum 6-month retention for deployers (Art. 26(6)). Logs must record operational period, reference database, input data, and decisions made. | Configure logging before deployment. Establish log retention controls. Export capability mandatory for market surveillance authority requests. |
| Art. 13 | Transparency | Users must receive instructions sufficient to interpret outputs and exercise oversight. Capabilities, limitations, intended purpose, foreseeable misuse scenarios, and accuracy levels all documented. | Ensure user-facing documentation is accurate, current, and accessible to all affected parties including workers. |
| Art. 14 | Human oversight | System designed to allow effective oversight. Named person(s) with competence, training, authority, and support to interpret outputs, override results, or halt the system when necessary. | Assign named oversight person. Document competence. Configure technical override capability. Train. Record interventions. |
| Art. 15 | Accuracy, robustness, cybersecurity | Appropriate accuracy level declared and documented. Resilience to errors, faults, and adversarial inputs designed in. Cybersecurity measures proportionate to the system’s risk profile. | Obtain accuracy and robustness certifications. Include AI security testing in cybersecurity programme and vendor contracts. |
| Art. 73 | Serious incident reporting | Notify market surveillance authority of serious incidents or malfunctions affecting safety or fundamental rights. Mandatory, time-bound, with documented investigation and remediation evidence. | Build AI incident response procedure. Define serious incident threshold. Establish and test notification workflow before incidents occur. |
Identity governance in scope
SAP environments using AI-assisted provisioning, SoD analytics, automated ReCertification, or HCM connectors may qualify as high-risk AI deployments under Annex III (employment and worker management).
HR and worker management AI
- CV sorting, candidate ranking, and worker management are explicitly named high-risk (Annex III §4).
- Automated access decisions affecting worker system privileges require governance review and oversight assignment.
- SIVIS ReCertification workflows with AI scoring may trigger transparency or high-risk classification depending on decision weight and worker impact.
- Any AI influencing hiring, termination, performance evaluation, or task allocation falls in scope.
Control design for IAM teams
- Document all AI-assisted decision flows in the corporate AI register with risk classification rationale.
- Classify SIVIS/SAP automation against the four-tier model — provisioning influencing access to sensitive systems likely qualifies as high-risk.
- Implement Art. 12-compliant logging, Art. 14 oversight checkpoints, and quarterly review cadence.
- Ensure audit trails are tamper-evident and exportable for regulator, internal audit, and market surveillance use.
AI Act governance roadmap
Four phases for building AI governance capability ahead of the August 2026 enforcement milestone — structured for Compliance Officers and CISOs leading internal programmes.
Inventory
Build a corporate AI register: every system, vendor-supplied and internal. Capture purpose, data inputs, decision outputs, business owner, and training data provenance.
Classify
Map each system to the four-tier risk model. Identify unacceptable, high-risk, and transparency-risk use cases. Document classification rationale with Annex III reference.
Control design
Design data governance, Art. 14 oversight assignments, Art. 12 logging architecture, Art. 13 transparency disclosures, and Art. 73 incident response procedures per risk level.
Audit readiness
Assemble audit evidence packages: Art. 11 technical file, Art. 12 logs, Art. 14 oversight records, Art. 73 incident log. Internal audit before regulator engagement.
NIS2 Compliance 2026
The NIS2 Directive mandates robust cybersecurity risk management, 24–72 hour incident reporting, direct management liability, and fines up to €10M for essential and important entities across 18 critical sectors in the EU.
22% of breaches start with a stolen credential — NIS2 closes that gap with liability.
Essential & important entities
NIS2 classifies medium-to-large organisations into two tiers. Essential entities face proactive supervision and higher fines. Important entities face reactive supervision triggered by incidents or evidence of non-compliance.
EnergyEssential
Electricity, oil, gas, hydrogen production, distribution, and supply.
TransportEssential
Air, rail, water, and road transport operators and infrastructure managers.
Banking & FinanceEssential
Credit institutions, financial market infrastructure, and central banks.
HealthcareEssential
Hospitals, healthcare providers, EU reference laboratories, pharma R&D.
WaterEssential
Drinking water suppliers, distributors, and wastewater operators.
Digital InfrastructureEssential
Cloud providers, data centers, DNS, TLD registries, internet exchange points.
Public AdministrationEssential
Central government and critical public bodies designated by member states.
ManufacturingImportant
Medical devices, automotive, machinery, electrical equipment manufacturers.
Postal ServicesImportant
Postal and courier service operators across the EU.
ChemicalsImportant
Chemical manufacturing, production, and distribution entities.
Digital ProvidersImportant
Online marketplaces, search engines, social networking platforms.
ResearchImportant
Research organisations as designated under national implementation.
Every measure — every control
Article 21 requires appropriate technical and organisational measures to manage cybersecurity risks. Each measure below includes the satisfying control, the audit evidence it generates, and IAM/SAP relevance.
| Art. 21 Measure | Requirement | Satisfying Control | Audit Evidence | IAM / SAP Relevance |
|---|---|---|---|---|
| Access control | Role-based access, least privilege, credential security policies | Password manager with RBAC, SIVIS WebManager roles, regular access review cadence | Role assignment logs, access reports, recertification records | ✓ Core SIVIS function |
| Multi-factor authentication | MFA enforced for all privileged and remote access | MFA policy, SSO integration, privileged access workstations, exception register | MFA enforcement reports, login logs, exception register | ✓ SIVIS SSO / SAP Auth |
| Incident reporting | 24h initial warning, 72h full report, 1-month final report | SIEM, CSIRT notification workflow, incident response plan, tabletop exercises | Incident tickets, notification timestamps, communications log | ⚡ SAP audit log feeds |
| Supply chain security | Assess cybersecurity posture of vendors and suppliers | Vendor risk assessment framework, contract security clauses, third-party audit rights | Supplier questionnaires, risk register, contract register | ⚡ KUKA / Pointsharp chain |
| Encryption | Encryption of data at rest and in transit; cryptographic key management | AES-256, TLS 1.3 enforced, HSM key management, certificate inventory | Encryption policy, certificate inventory, key management procedure | ✓ SAP secure channels |
| Vulnerability management | Identify, prioritise, and remediate vulnerabilities continuously | Regular patching, CVE scanning, CVSS-scored remediation SLA, penetration testing | Patch management logs, scan reports, remediation evidence | ⚡ SAP transport hygiene |
| Business continuity | Backup management, disaster recovery, crisis management | Tested BCP/DRP, off-site backup, RTO/RPO targets defined and tested | BCP test records, backup logs, RTO/RPO test evidence | ✓ SENTINEL emergency access |
| Cyber hygiene & training | Basic cyber hygiene practices and cybersecurity training for all staff | Annual security awareness training, phishing simulation, AI literacy programme | Training completion records, phishing statistics, competence assessments | ✓ SIVIS training series |
| Cryptographic policies | Policies on use of cryptography; algorithm governance | Cryptographic standards policy, approved algorithm list, deprecated algorithm ban | Policy document, algorithm inventory, review records | ⚡ SAP ABAP crypto review |
| HR security | Background checks, security vetting, NDA; joiners/movers/leavers process | HR security policy, onboarding/offboarding checklist, access revocation SLA | Signed NDAs, vetting records, access revocation evidence | ✓ SoD joiner/mover/leaver |
5-phase deployment to audit readiness
Organisations with structured tooling can achieve NIS2 audit readiness within 30 days — deploy access control and audit logging first, then close each Article 21 gap systematically.
Assess
Gap analysis against Article 21. Map current controls to each measure. Identify critical deficiencies.
Audit
Run credential security audit. Review privileged access, shared accounts, and password policies.
Deploy
Deploy password manager with RBAC and audit logging. Enforce MFA. Document credential policies.
Configure
Configure RBAC, LDAP/AD mapping, SIEM integration, and incident notification workflow.
Monitor
Continuous monitoring, quarterly review cadence, and log export for evidence packages.
Early warning
Submit initial early warning to national competent authority or CSIRT. Indicate whether unlawful/malicious acts suspected and whether cross-border impact exists.
Incident notification
Update with initial assessment: severity, likely impact, indicators of compromise, affected systems. Include preliminary root cause where available.
Final report
Detailed description, threat type, applied and planned mitigating measures, cross-border impact assessment, any regulatory follow-up required.
AI Compliance Auditor Standards
The auditor’s role in AI governance has been formally defined by IIA IPPF 2024, ISACA AAIA 2025, AICPA/PCAOB AI guidance, and the EU AI Act itself. These converge on one requirement: a named human exercising professional judgment that no algorithm can replicate.
IIA Global Internal Audit Standards 2024
IPPF · Effective January 9, 2025 · Five domains · 15 guiding principles
ISACA Advanced in AI Audit (AAIA) 2025
First AI audit-specific certification · Prerequisites: CISA, CIA, or CPA
Professional judgment
Defining materiality, acceptable risk, and ethically legitimate trade-offs. No algorithm encodes this — it is the irreplaceable contribution of the trained professional auditor operating under IIA IPPF 2024.
Algorithmic skepticism
AI outputs carry the illusion of precision. Auditors must interrogate model logic, data lineage, and output assumptions — not accept numerical outputs because they appear authoritative or computationally confident.
Contextual intelligence
Interpreting power dynamics, business reality, and stakeholder consequences. Algorithms flag anomalies; only auditors determine whether an anomaly reflects a control failure, business change, or intentional override.
Personal accountability
Only people bear responsibility before regulators, boards, and courts. EU AI Act Art. 14 makes this structural: the oversight person must exist, be named, be trained, and be empowered to stop the system.
| Standard | Scope | AI Audit Application | Key Evidence Requirement |
|---|---|---|---|
| ISA 315 | Risk identification & assessment | Technology risk assessment including AI system controls. Auditors must understand AI processing flows, not just financial outputs. AI introduces a new category of IT-related control risk requiring specific assessment. | AI system inventory, risk classification, control design documentation, system flow diagrams. |
| ISA 330 | Responding to assessed risks | Tests of controls over AI systems — operating effectiveness testing of human oversight, logging integrity, model version controls, and approval workflows. Tests proportionate to assessed risk level. | Test of controls workpapers, operating effectiveness evidence, override log review results. |
| ISA 500 | Audit evidence | Defines sufficient, appropriate audit evidence. AI model cards, technical files, logging outputs, and oversight records are primary evidence. Verbal representations and policy documents alone are insufficient. | Evidence completeness checklist; all Art. 9–15 artifacts obtained and evaluated for sufficiency. |
| ISA 540 | Auditing accounting estimates | AI-driven financial estimates require evaluation of model assumptions, data inputs, and sensitivity analysis. Auditors must test whether the AI model is appropriate for the accounting estimate it generates. | Model documentation, assumption log, sensitivity test results, independent recalculation records. |
| ISAE 3000 | Assurance on non-financial subjects | Framework for assurance on AI governance and control effectiveness where no financial audit is involved — applicable to regulatory compliance attestations, AI ethics reports, and EU AI Act deployer statements. | Assurance engagement documentation, evidence evaluation, conclusion rationale, independence declaration. |
AI Audit Frameworks — PwC · EY · KPMG · Deloitte
The four largest audit and advisory firms have each developed structured AI audit methodologies aligned to the EU AI Act and international standards. These represent the market benchmark for what a professional AI governance review looks like in 2025–2026.
PwC’s Responsible AI framework organises governance across five dimensions: fairness, interpretability, robustness, transparency, and data governance. The EU AI Act has been mapped directly to this framework with Art. 9–15 obligations assigned to specific pillars.
- AI Trust Index — maturity assessment against 47 governance indicators
- AI System Inventory — structured classification against EU AI Act Annex III
- Human-in-the-loop design review — tests Art. 14 oversight assignment completeness
- AI Ethics Board governance design — board-level accountability architecture
- Third-party AI risk assessment — vendor AI supply chain due diligence
EY’s Trusted AI addresses governance across six principles: human agency, technical robustness, privacy, transparency, fairness, and societal wellbeing. Since 2024, EY has aligned explicitly to EU AI Act Art. 9–15 and ISO/IEC 42001.
- AI compliance diagnostic — gaps against EU AI Act Annex IV technical file
- DPIA/HRIA acceleration — AI-specific GDPR-aligned impact assessment
- AI model risk review — independent technical and governance review
- AI incident response design — Art. 73 reporting workflow architecture
- Board AI literacy programme — executive education mapped to IIA IPPF 2024
KPMG’s AI governance practice combines regulatory compliance advisory with internal audit transformation. The EU AI Act readiness assessment is structured around the Art. 9–15 obligation matrix and ISO/IEC 42001 architecture.
- AI Register methodology — inventory with risk classification template
- ISO 42001 readiness assessment — gap analysis against all clauses
- AI internal audit transformation — redesigning IA for AI-era assurance
- NIST AI RMF implementation — GOVERN/MAP/MEASURE/MANAGE model
- AI supply chain risk — third-party assessment for deployer obligations
Deloitte’s Trustworthy AI™ maps governance controls to legal obligations, ethical principles, and operational effectiveness. EU AI Act deployer compliance playbooks published since Q4 2024.
- EU AI Act Deployer Assessment — 120-question readiness review vs Art. 9–15
- AI Model Risk Management — Three Lines of Defence for AI oversight
- Bias and fairness audit — demographic impact testing methodology
- AI incident management — Art. 73 workflow and SIEM integration design
- Continuous AI auditing — automated evidence collection and monitoring
Universal AI Audit Requirements — What Every Firm Demands
Governance documentation
Minimum baseline evidence — all four firms:
- Named AI system inventory with risk classification
- Written governance policy with board approval
- Named accountability owners per AI system
- Risk appetite statement for AI
- Prohibited use case register (named)
- Model card for every high-risk system
- Technical file per Art. 11 / Annex IV
- DPIA / HRIA completed and signed off
Operational controls
Evidence governance is operational, not just documented:
- Art. 12-compliant tamper-evident logging
- Human oversight records with override evidence
- Model version control and change management
- Continuous monitoring with defined thresholds
- Bias and fairness testing results on record
- Post-deployment monitoring logs current
- Supplier / vendor AI documentation obtained
- Art. 73 incident register and reporting evidence
Assurance & testing
Independent testing and evidence validation:
- Internal audit programme over AI governance
- Art. 14 human oversight effectiveness test
- Accuracy and robustness validation records
- Data lineage and provenance verification
- Third-party AI vendor due diligence evidence
- Board-level AI literacy attestation
- Regulatory notification readiness drill results
- Annual governance review and board report
Business & Operations
AI system owners, product teams, data scientists, and IT operations. Own the risk and manage day-to-day AI operations.
- AI register maintenance and currency
- Art. 12 log management and retention
- Art. 14 oversight execution and records
- Incident detection and initial response
Risk, Compliance & Legal
CRO, CCO, DPO, and Legal. Design the AI governance framework, validate classifications, and monitor enterprise compliance.
- Risk classification validation
- DPIA / HRIA sign-off and review
- Regulatory liaison and Art. 73 notifications
- AI governance policy ownership and update
Internal Audit
Independent assurance over design and operating effectiveness. ISACA AAIA and IIA IPPF 2024 define this as a distinct discipline requiring dedicated competence.
- AI governance audit programme
- Art. 9–15 compliance testing
- Oversight effectiveness review and testing
- Board reporting on AI assurance
Documentation systems — eight required types
Eight document types constitute the minimum audit evidence package for any high-risk AI system. Each has a legal basis, mandatory content specification, and a defined audience. These are required by law or professional standard — not optional best practice.
| Document Type | Legal / Standard Basis | Mandatory Content | Primary Audience | Priority |
|---|---|---|---|---|
| Model Card | EU AI Act Art.11,13 · ISO 42001 §6.1 · NIST AI RMF GOVERN 1.7 | Intended use, limitations, evaluation metrics, bias testing results, performance across demographic groups, version history, foreseeable misuse scenarios | Auditors, Regulators, Deployers, Impacted Individuals | CRITICAL |
| DPIA / HRIA | EU AI Act Art.27 · GDPR Art.35 · ISO 27701 §7.2 | Risk identification, rights impact assessment, mitigation measures, residual risk, DPO sign-off, review schedule, consultation records | DPO, Legal, Risk, Supervisory Authority | CRITICAL |
| Technical File (Annex IV) | EU AI Act Art.11, Annex IV · ISO 42001 §8.4 | System description, design specs, training data provenance, testing methodology, performance benchmarks, version history, conformity assessment evidence | Notified Bodies, Market Surveillance, Internal Audit | HIGH |
| System Log | EU AI Act Art.12 · ISO 27001 A.8.15 · ISA 315 §A81 | Input/output records, decision logs, override events, anomaly flags — minimum 6-month tamper-evident retention; exportable on regulatory request | Auditors, Incident Response, Compliance, Market Surveillance | HIGH |
| Transparency Report | EU AI Act Art.13 · NIST AI RMF MAP 5.2 · ISO 42001 §9.3 | Capabilities and limitations, human oversight measures, safe use instructions, accuracy/robustness levels, foreseeable misuse scenarios | Users, Board, Public (where applicable) | HIGH |
| Incident Register | EU AI Act Art.73 · ISO 27001 A.5.24 · IIA IPPF Std.2400 | Incident description, severity, root cause, corrective action, closure evidence, regulatory notification timestamp, market surveillance communications | CISO, Audit Committee, Regulator | HIGH |
| Governance Policy | EU AI Act Art.9 · ISO 42001 §5.2 · NIST AI RMF GOVERN 1.1 | Risk appetite, prohibited use cases (named), accountability assignments, review cycle, training requirements, enforcement mechanism | Board, Senior Leadership, All Staff | HIGH |
| Audit Evidence Package | IIA Std.2330 · ISA 500 · ISO 42001 §9.2 · EU AI Act composite | All above plus: test plans, test results, bias assessment, oversight evidence, approval chains, remediation proof, board attestation | Internal/External Auditors, Regulators, Certification Bodies | CRITICAL |
| Control Area | Test Procedure | Evidence Required | Failure Signal |
|---|---|---|---|
| Model Inventory | Trace deployed version to approved model package; reconcile registry against live deployments | Signed model package, version log, change tickets, promotion approvals | Production model differs from approved — version mismatch, no change record |
| Human Oversight (Art. 14) | Sample AI decisions; test override/review capability; verify named oversight person trained and active | Override logs, reviewer IDs, SOPs, training records, competence assessment, intervention evidence | No mechanism to stop or escalate — oversight is nominal, not operational |
| Data Governance (Art. 10) | Reperform sample data lineage from source to model; verify data quality controls in place | Data dictionary, lineage maps, source approvals, preprocessing log, retention policy | Unknown data origin, undocumented preprocessing, expired retention controls |
| Bias Controls | Review protected-attribute testing results; verify thresholds were set, monitored, and acted on | Fairness metrics, threshold rationale, demographic test data, remediation action log | No adverse impact monitoring — or testing performed but findings not acted on |
| Transparency (Art. 13) | Compare user-facing notice to actual model behaviour and declared limitations | User notices, FAQs, UI screenshots, model card, AI disclosure statements | Users cannot identify AI involvement, understand decisions, or initiate challenge |
| Incident Response (Art. 73) | Walk one live AI incident end-to-end through response process; test SLA compliance | Ticket trail, escalation SLA evidence, root-cause analysis, fix evidence, retest results | Defect identified with no corrective action trail — or SLA breached with no rationale |
“No evidence = No control. Organisations will not be judged by how much AI they deploy — they will be judged by how well they can prove control, transparency, security, and accountability.”
| Threat | Description | Business Impact | Detection Method | Audit Evidence |
|---|---|---|---|---|
| Prompt Injection | Malicious input manipulates model instructions or retrieved context to bypass guardrails | Unauthorized actions, data exfiltration, reputational damage | Input validation logs, anomaly detection, red team tests | Test scripts, findings register, guardrail config |
| Data Poisoning | Malicious content inserted into training or RAG sources shifts model behaviour | Biased decisions, compliance failures, systematic errors | Data lineage audits, statistical drift monitoring | Data governance records, source approval logs |
| Training Data Leakage | Sensitive training content surfaces through model outputs or inversion attacks | Privacy breach, regulatory fines, IP loss | Output monitoring, DPIA review, membership inference tests | DPIA, data minimisation policy, test results |
| Model Theft | Attackers replicate model weights, prompts, or behaviour via API abuse | IP loss, competitive harm, revenue impact | API rate-limit alerts, access log analysis | API access logs, rate-limit configs, IP controls |
| Supply Chain Attacks | Compromised open-source models, plugins, or vector sources contaminate production | Widespread compromise, undetected backdoors | Software composition analysis, vendor attestations | SCA reports, vendor due diligence, SBOM |
| Hallucination Risk | False outputs drive bad approvals, misleading advice, or inaccurate control decisions | Wrong business decisions, regulatory violations | Output validation gates, human review sampling | Validation logs, human oversight records, error reports |
| Unauth. Fine-Tuning | Shadow model changes invalidate approved behaviour and control assumptions | Control bypass, undocumented risk exposure | Model versioning, change management controls | Model registry, CAB records, change tickets |
| Privilege Escalation | AI agents with tools or memory perform actions beyond intended authority | Unauthorized transactions, data access, system changes | Agent activity logs, permission boundary alerts | IAM logs, agent scope policy, workflow approvals |
| Activity | Business | IT | Security | Audit | Governance Note |
|---|---|---|---|---|---|
| AI Strategy & Policy | A | C | C | I | Business owns AI strategy. Audit provides independent challenge. |
| System Inventory & Classification | C | A | C | I | IT owns system registry. Audit verifies completeness. |
| Risk Assessment (pre-deployment) | C | C | A | I | Security leads risk assessment. Audit reviews methodology. |
| Technical Documentation (Art. 11) | I | A | C | I | IT responsible. Audit verifies existence and completeness. |
| Named Human Oversight (Art. 14) | A | C | I | I | Business assigns named oversight. IT configures override mechanism. |
| Continuous Monitoring | I | A | A | C | IT and Security jointly accountable. Audit consulted on design. |
| Incident Response (Art. 73) | I | C | A | C | Security owns incident response. Audit consulted on root cause. |
| Independent Assurance | I | I | C | A | Audit is the only function independently accountable for assurance. |
| Annual Governance Review | C | C | C | A | Audit accountable for annual review. Board receives report. |
AI Act + NIS2 + Audit Standards — Unified compliance
Most CEE enterprises subject to the EU AI Act are also NIS2 entities. The two frameworks share common control building blocks — IAM, audit logging, risk registers, incident reporting — making a unified programme more efficient than two parallel workstreams.
Maximum for prohibited practices (Art. 5). €15M / 3% for GPAI and high-risk obligations. €7.5M / 1.5% for information failures to authorities. Whichever is higher — fixed amount or percentage of global annual turnover.
Maximum for essential entities. €7M / 1.4% for important entities. Management can be personally fined and temporarily barred from management roles. Proactive supervision for essential; reactive for important entities.
Dual scope assessment
Determine NIS2 classification (essential/important). Inventory AI systems against the four-tier risk model. Create a single integrated compliance gap register covering both frameworks. Avoid two parallel gap registers — they produce contradictory remediation plans.
Unified control design
Design IAM controls, logging architecture, and risk management procedures satisfying both NIS2 Article 21 and AI Act Art. 9–15 simultaneously. ISO 42001 provides the integrating management system. NIST AI RMF provides the operational operating model.
Assurance & evidence
Build exportable audit evidence packages covering both frameworks. Train the board on dual executive liability. Establish quarterly review cadence feeding both the AI governance committee and the CISO’s NIS2 compliance dashboard.
| ISO 42001 Clause | Requirement | EU AI Act Alignment | NIS2 Alignment | Big 4 / IIA Requirement |
|---|---|---|---|---|
| §4 | Context of organisation | Defines scope for Art. 9 risk management system | Organisation classification (essential/important) | Mandatory starting point — all Big 4 frameworks |
| §5.2 | AI policy | Satisfies Art. 9 organisational risk management requirement | Governance policy for cybersecurity risk management | IIA IPPF Domain I: governance design |
| §6.1 | AI risk assessment | Art. 9 continuous risk management; Art. 10 data governance | NIS2 Art. 21 risk assessment requirements | KPMG / Deloitte: risk register methodology |
| §8.4 | AI system documentation | Art. 11 technical file; Annex IV requirements | Technical documentation for audit evidence | PwC / EY: Annex IV compliance assessment |
| §9.2 | Internal audit | Art. 9 governance oversight; Art. 73 assurance | Independent verification of NIS2 controls | IIA IPPF Std.2330; ISA 500: evidence sufficiency |
| §9.3 | Management review | Art. 13 transparency; board governance | Board-level NIS2 oversight and liability management | All Big 4: annual board AI governance report |
| §10 | Improvement | Art. 9 continuous lifecycle; post-market monitoring | Post-incident improvement; NIS2 lessons learned | ISACA AAIA Domain 2: model lifecycle management |
Validated sources for executive and audit use
All references are primary regulatory sources, official professional standards, or authoritative firm publications. Primary Commission sources are the strongest citations for board, audit committee, and regulatory briefings.
EU AI Act — Official text (Regulation 2024/1689)
Full text including Annex III (high-risk categories), Annex IV (technical documentation), Art. 9–15 obligations, Art. 73 incident reporting, and Art. 99 penalties.
European Commission — EU AI Act hub
Implementation timeline, AI Office resources, GPAI Code of Practice, and harmonised standards development updates including August 2026 milestone.
AI Act Service Desk — Implementation timeline
Official phased roll-out: 2 Feb 2025, 2 Aug 2025, 2 Aug 2026, December 2027, August 2028 milestones with AI Omnibus updates.
NIS2 Directive (EU 2022/2555) — Official text
Full directive including Article 21 measures, Article 23 incident reporting, management accountability provisions, and penalty framework.
ENISA — NIS2 implementation guidance
Technical guidance, sector-specific implementation notes, and Art. 21 cybersecurity measures. Authoritative for CEE national implementations and sector-specific regulatory dialogue.
IIA — Global Internal Audit Standards 2024 (IPPF)
Effective January 9, 2025. Five domains, 15 guiding principles. Standards 4.2, 4.3, 2.2, 2010, 2330 directly applicable to AI governance audit work.
ISACA — Advanced in AI Audit (AAIA) 2025
First AI audit-specific certification. Three domains: AI Governance & Risk, AI Operations, AI Auditing Tools & Techniques. Prerequisites: active CISA, CIA, or CPA.
ISO/IEC 42001 — AI Management System Standard
International AI management system standard. Integrates with ISO 27001, ISO 31000, and ISO 9001. Provides the governance operating system for unified AI Act and NIS2 compliance.
NIST AI Risk Management Framework (AI RMF 1.0)
GOVERN · MAP · MEASURE · MANAGE. Endorsed by all Big 4 firms for AI risk management. Aligns to EU AI Act Art. 9 continuous risk management obligation.
Article 99 penalties — AI Act reference
€35M/7% for Art. 5 prohibited practices · €15M/3% for Art. 10–15 and GPAI · €7.5M/1.5% for information failures.
European Commission — NIS2 Directive overview
Scope, key obligations, incident reporting, management liability, and harmonization resources from the Commission’s digital policy directorate.
Guardian Group Agency — AI Governance & SAP IAM Practice
SIVIS Pointsharp IAM, SAP SoD, EU AI Act advisory, NIS2, and AI-augmented documentation for multinational manufacturing and technology clients in CEE.