When your CISO mentions “AI safety” within the subsequent board assembly, what precisely do they imply? Are they speaking about defending your AI techniques from assaults? Utilizing AI to catch hackers? Stopping staff from leaking information to an unapproved AI service? Guaranteeing your AI doesn’t produce dangerous outputs?
The reply could be “the entire above”; and that’s exactly the issue.
AI turned deeply embedded in enterprise operations. Because of this, the intersection of “AI” and “safety” has develop into more and more advanced and complicated. The identical phrases are used to explain basically completely different domains with distinct aims, resulting in miscommunication that may derail safety methods, misallocate sources, and depart 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 convey readability to AI safety conversations throughout enterprise operations.
The Communication Problem
Contemplate this situation: your govt workforce asks you to current the corporate’s “AI safety technique” on the subsequent board assembly. With no frequent framework, every stakeholder might stroll into that dialog with a really completely different interpretation of what’s being requested. Is the board asking about:
- Defending your AI fashions from adversarial assaults?
- Utilizing AI to boost your risk detection?
- Stopping information leakage to exterior AI providers?
- Offering guardrails for AI output security?
- Guaranteeing regulatory compliance for AI techniques?
- Defending in opposition to 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
With no shared framework, organizations wrestle 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 completely different considerations, includes completely different risk actors, requires completely different controls, and usually falls beneath completely 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 at the side of the others to create a complete AI technique.
These 5 domains don’t exist in isolation; they reinforce and depend upon each other and have to be deliberately aligned. Study extra about every area within the paper, which is meant as a place to begin for trade 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 reminiscent of NIST SP 800-53 and ISO 27001, this taxonomy is just not meant to work in isolation of extra detailed frameworks, however relatively to offer frequent vocabulary throughout trade.
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 trade frameworks, such because the Coalition for Safe AI (CoSAI) Threat Map, MITRE ATLAS, and others.
The intersection of AI and safety is just not a single downside to unravel, however a constellation of distinct threat domains; every requiring completely 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 threat comprehensively throughout all related domains
- Assign accountability clearly to the proper groups
- Make investments strategically relatively than reactively
