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? Making certain your AI doesn’t produce dangerous outputs?
The reply is perhaps “all the above”; and that’s exactly the issue.
AI turned deeply embedded in enterprise operations. In consequence, the intersection of “AI” and “safety” has turn into more and more complicated and complicated. The identical phrases are used to explain essentially completely different domains with distinct goals, resulting in miscommunication that may derail safety methods, misallocate assets, 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 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 state of affairs: your govt crew 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 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 knowledge leakage to exterior AI providers?
- Offering guardrails for AI output security?
- Making certain 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 mistaken goals
And not using a 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 sometimes falls underneath 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 rely on 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 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 offers a typical language to speak in regards to the domains of cybersecurity whereas not eradicating the necessity for detailed cybersecurity framework comparable to NIST SP 800-53 and ISO 27001, this taxonomy is just not meant to work in isolation of extra detailed frameworks, however moderately to supply widespread vocabulary throughout business.
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 business 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 resolve, 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 considerations with out ambiguity
- Assess danger comprehensively throughout all related domains
- Assign accountability clearly to the best groups
- Make investments strategically moderately than reactively