When your CISO mentions “AI safety” within the subsequent board assembly, what precisely do they imply? Are they speaking about defending your AI programs from assaults? Utilizing AI to catch hackers? Stopping staff from leaking knowledge to an unapproved AI service? Making certain your AI doesn’t produce dangerous outputs?
The reply is likely to 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 basically completely different domains with distinct goals, resulting in miscommunication that may derail safety methods, misallocate sources, and go away essential 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 deal with this problem head-on. Collectively, we introduce a five-domain taxonomy designed to deliver readability to AI safety conversations throughout enterprise operations.
The Communication Problem
Take into account this situation: your govt staff asks you to current the corporate’s “AI safety technique” on the subsequent board assembly. With out a frequent framework, every stakeholder could 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 reinforce your menace detection?
- Stopping knowledge leakage to exterior AI providers?
- Offering guardrails for AI output security?
- Making certain regulatory compliance for AI programs?
- 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 flawed goals
With out 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 completely different issues, includes completely different menace actors, requires completely different controls, and sometimes 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 together with the others to create a complete AI technique.
These 5 domains don’t exist in isolation; they reinforce and depend upon each other and should be deliberately aligned. Be taught 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 lengthen the taxonomy to their particular contexts whereas preserving the core distinctions between domains.
Framework Alignment
Simply because the NIST Cybersecurity Framework supplies a typical language to speak concerning 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 reasonably to supply frequent vocabulary throughout trade.
As such, the paper builds on Cisco’s Built-in AI Safety and Security Framework not too long ago launched by my colleague Amy Chang. It additionally aligns with established trade frameworks, such because the Coalition for Safe AI (CoSAI) Danger Map, MITRE ATLAS, and others.
The intersection of AI and safety is just not a single drawback to unravel, however a constellation of distinct danger 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 issues with out ambiguity
- Assess danger comprehensively throughout all related domains
- Assign accountability clearly to the correct groups
- Make investments strategically reasonably than reactively
