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AI Security

What is AI Security?

Measures to protect AI systems from adversarial attacks, data poisoning, model theft, and other threats specific to machine learning systems.

AI and Technology

Each of these is named in at least one of the same controls as ai security. The number is how many controls name both.

What the standards actually require on ai security

Requirements naming ai security across 4 standards, quoted from the control text.

Security (Sekyuritii セキュリティ) is the fifth of 10 Principles per Japan AI Guidelines for Business + addresses cybersecurity throughout AI lifecycle including adversarial attacks specific to ML + traditional cyber threats to AI infrastructure.

JP-AIG-Security-Adversarial-Attack-Protection-Prompt-Injection-Data-Poisoning-Model-Extraction-AISI-Red-Team · Japan AI Guidelines Security + Adversarial Attack Protection + Prompt Injection + Data Poisoning + Model Extraction + Membership Inference + AISI Red-Team + MLSecOps + Supply Chain Security + Foundation Model Vulnerabilities

Mitigate CBRN information or capabilities risks per NIST AI 600-1 RISK-01 including: training data filtering for chemical + biological + radiological + nuclear hazardous content + model evaluation against WMDP benchmarks + safety filters at inference + uplift...

NISTAI600-6 · CBRN, Cybersecurity, and Information Security Risks (Risks 1, 9)

Adhere to OECD 2024 Update security + model and data protection + IP + environmental sustainability. Security including model and data protection must (a) protect AI models against extraction + theft + tampering + with model signing + cryptographic protection...

OECDAI24-6 · Security, Model and Data Protection, Intellectual Property, and Environmental Sustainability

Questions people ask about ai security

What is AI Security?
Measures to protect AI systems from adversarial attacks, data poisoning, model theft, and other threats specific to machine learning systems.
Why is AI Security important for compliance?
AI Security is a key concept in AI and Technology. Understanding ai security helps organizations meet regulatory requirements, reduce risk, and demonstrate due diligence during audits. Our compliance platform maps 686 frameworks with 311K cross-framework control mappings.
Which compliance frameworks address AI Security?
AI Security appears in the requirement text of Hong Kong Personal Data (Privacy) Ordinance (PDPO, Cap 486), Japan AI Guidelines, NIST AI 600-1: Generative AI Profile, OECD Recommendation on Artificial Intelligence (2024 Update). Across these standards we have identified 4 controls that name it directly, each linked to the control text on the compliance platform.
Where can I learn more about AI Security?
Explore our compliance framework pages to see how ai security applies across different standards and regulations. Our implementation guides provide step-by-step guidance, and the compliance platform offers AI-powered analysis of how this concept maps across 686 frameworks.

See how AI Security applies across compliance frameworks

Our platform maps 686 frameworks with 311K cross-framework control mappings. Explore how this concept is addressed across standards.

Written and maintained by Gerard Blokdyk, The Art of Service.