EU AI Act EUAI-Art.15: Accuracy, robustness and cybersecurity

What else in your programme already covers this

This control maps to 34 controls across 10 other frameworks. If you already hold one of them, the evidence you collected for it is the starting point here rather than new work.

NIST SP 800-53 Rev 5 · 7 controls

NIST800-PL-8 PL-8 Security and Privacy Architectures
NIST800-SA-11 SA-11 Developer Testing and Evaluation
NIST800-SA-8 SA-8 Security and Privacy Engineering Principles
NIST800-SI-10 SI-10 Information Input Validation
NIST800-SI-2 SI-2 Flaw Remediation
NIST800-SI-6 SI-6 Security and Privacy Function Verification
NIST800-SI-7 SI-7 Software, Firmware, and Information Integrity

SOC 2 · 6 controls

SOC2-A1.1 A1.1 Managing processing capacity
SOC2-CC5.2 CC5.2 General controls over technology (COSO principle 11)
SOC2-CC6.6 CC6.6 Protection against threats from outside the system boundary
SOC2-CC6.8 CC6.8 Preventing and detecting unauthorised or malicious software
SOC2-PI1.3 PI1.3 Controls over system processing
SOC2-PI1.4 PI1.4 Controls over output delivery

Cloud Security Alliance Cloud Controls Matrix (CCM) v4.0.1 · 5 controls

CCM-AIS-02 Application Security Baseline Requirements
CCM-AIS-04 Secure Application Design and Development
CCM-AIS-05 Automated Application Security Testing
CCM-DSP-07 Data Protection by Design and Default
CCM-TVM-07 Vulnerability Identification

ISO 27001:2022 · 4 controls

8.26 Application security requirements
8.27 Secure system architecture and engineering principles
8.29 Security testing in development and acceptance
8.8 Management of technical vulnerabilities

NIST AI Risk Management Framework (AI RMF 1.0) · 4 controls

AIRMF-MS-1.1 Approaches and metrics for measurement of AI risks enumerated during the MAP function are selected for implementation starting with the most significant AI risks, and the risks or trustworthiness characteristics that will not or cannot be measured are properly documented
AIRMF-MS-2.5 The AI system to be deployed is demonstrated to be valid and reliable, and limitations of the generalizability beyond the conditions under which the technology was developed are documented
AIRMF-MS-2.6 AI system is evaluated regularly for safety risks as identified in the MAP function, is demonstrated to be safe, its residual negative risk does not exceed the risk tolerance, and it can fail safely, particularly if made to operate beyond its knowledge limits
AIRMF-MS-2.7 AI system security and resilience as identified in the MAP function are evaluated and documented

NIS2 Directive · 3 controls

Art.21.2.g Basic cyber hygiene practices and cybersecurity training
Art.21.2.h Policies and procedures on the use of cryptography and, where appropriate, encryption
Art.21.2.i Human resources security, access control policies and asset management

ISO/IEC 42001:2023 · 2 controls

A.6.2.4 AI system verification and validation
A.6.2.6 AI system operation and monitoring

DORA · 1 control

DORA-Art.9 Protection and prevention

EU Cyber Resilience Act · 1 control

CRA-Art.11_12 Relationship with general product safety and AI Act (Articles 11-12)

GDPR · 1 control

GDPR-Art.32 Security of processing

Every mapping shown was judged rather than inferred from wording similarity, and the ones that failed review are published too. See the coverage reports and what was rejected.

You are reading one control. How much of EU AI Act have you already done?

EU AI Act EUAI-Art.15 is one control. If you already hold one of the frameworks below, a reviewed crosswalk already says how much of EU AI Act your existing evidence covers. Hold ISO/IEC 42001:2023 and 17 of 43 EU AI Act controls already carry evidence.

If you hold ISO/IEC 42001:202339.5% covered · 26 gaps If you hold NIST AI Risk Management Framework (AI RMF 1.0)25.6% covered · 32 gaps

Each report names every control your existing framework evidences, every one it does not, the reasoning behind each claim, and the claims that were argued against and rejected. 0 were rejected on the ISO/IEC 42001:2023 pair alone.

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