When your CISO mentions “AI safety” within the subsequent board assembly, what precisely do they imply? Are they speaking about defending your AI methods from assaults? Utilizing AI to catch hackers? Stopping workers from leaking knowledge to an unapproved AI service? Guaranteeing your AI doesn’t produce dangerous outputs?

The reply could be “all the above”; and that’s exactly the issue.

AI turned deeply embedded in enterprise operations. Consequently, the intersection of “AI” and “safety” has develop into more and more complicated and complicated. The identical phrases are used to explain essentially totally different domains with distinct aims, resulting in miscommunication that may derail safety methods, misallocate sources, and go away important gaps in safety. We’d like a shared understanding and shared language.

Jason Lish (Cisco’s Chief Info Safety Officer) and Larry Lidz (Cisco’s VP of Software program Safety) co-authored this paper with me to assist tackle this problem head-on. Collectively, we introduce a five-domain taxonomy designed to carry readability to AI safety conversations throughout enterprise operations.

The Communication Problem

Think about this situation: your govt group asks you to current the corporate’s “AI safety technique” on the subsequent board assembly. And not using a widespread framework, every stakeholder could stroll into that dialog with a really totally different interpretation of what’s being requested. Is the board asking about:

• Defending your AI fashions from adversarial assaults?
• Utilizing AI to reinforce your risk detection?
• Stopping knowledge leakage to exterior AI providers?
• Offering guardrails for AI output security?
• Guaranteeing regulatory compliance for AI methods?
• Defending towards AI-enabled or AI-generated cyber threats? This ambiguity results in very actual organizational issues, together with:
• Miscommunication in govt and board discussions
• Misaligned vendor evaluations— evaluating apples to oranges
• Fragmented safety methods with harmful gaps
• Useful resource misallocation specializing in the incorrect aims

And not using a shared framework, organizations battle to precisely assess dangers, assign accountability, and implement complete, coherent AI safety methods.

The 5 Domains of AI Safety

We suggest a framework that organizes the AI-security panorama into 5 clear, deliberately distinct domains. Every addresses totally different considerations, includes totally different risk actors, requires totally different controls, and usually falls beneath totally different organizational possession. The domains are:

• Securing AI
• AI for Safety
• AI Governance
• AI Security
• Accountable AI

Every area addresses a definite class of dangerous and is designed for use along side the others to create a complete AI technique.

These 5 domains don’t exist in isolation; they reinforce and rely upon each other and should be deliberately aligned. Study extra about every area within the paper, which is meant as a place to begin for business dialogue, not a prescriptive guidelines. Organizations are inspired to adapt and prolong the taxonomy to their particular contexts whereas preserving the core distinctions between domains.

Framework Alignment

Simply because the NIST Cybersecurity Framework gives a standard language to speak in regards to the domains of cybersecurity whereas not eradicating the necessity for detailed cybersecurity framework resembling NIST SP 800-53 and ISO 27001, this taxonomy isn’t meant to work in isolation of extra detailed frameworks, however relatively to supply widespread vocabulary throughout business.

As such, the paper builds on Cisco’s Built-in AI Safety and Security Framework just lately launched by my colleague Amy Chang. It additionally aligns with established business frameworks, such because the Coalition for Safe AI (CoSAI) Threat Map, MITRE ATLAS, and others.

The intersection of AI and safety isn’t a single downside to unravel, however a constellation of distinct danger domains; every requiring totally different experience, controls, and organizational possession. By aligning with these domains with organizational context, organizations can:

• Talk exactly about AI safety considerations with out ambiguity
• Assess danger comprehensively throughout all related domains
• Assign accountability clearly to the appropriate groups
• Make investments strategically relatively than reactively