Artificial intelligence (AI) advancements bring significant existential risks (x-risks), such as model theft, infohazard leaks, and rogue AI systems. While top AI labs are crucial in mitigating these risks, they often struggle to find cybersecurity professionals who combine high technical expertise with a deep understanding of AI-related dangers. The competitive cybersecurity talent market doesn't prioritize these specialized concerns, leaving critical organizations vulnerable.

Redirecting and Upskilling Cybersecurity Talent

One way to address this gap is by creating a pipeline of cybersecurity professionals specifically prepared to tackle AI-related risks. This could involve two key approaches:

• Recruiting high-skilled cybersecurity experts and placing them in AI labs and other high-risk organizations (like biotech firms or policy groups), with incentives such as mission-driven work and competitive pay.
• Developing specialized training programs to upskill existing professionals, teaching them not only advanced cybersecurity practices but also AI-specific risks and mitigation strategies.

Since generic cybersecurity firms and existing training programs don't fully address x-risk alignment, there's room for a niche initiative focused on matching the right expertise with AI security challenges.

Execution and Validation

A simple starting point could be a pilot program placing a handful of pre-screened professionals in AI labs. Early feedback could refine recruitment and training methods before scaling up. Partnerships with labs and funders might help sustain the effort, either through fee-based placements or philanthropic support.

Key assumptions—like professionals being motivated by x-risk awareness and labs seeing long-term value—could be tested early with surveys and pilot collaborations. Over time, integrating AI-specific security curricula into existing training programs could expand the talent pool.

Compared to Existing Efforts

Traditional cybersecurity recruitment and training focus on general skills rather than AI-specific threats. This idea could differentiate itself by emphasizing:

• Tailored screening for x-risk alignment.
• Specialized training in model security and infohazard management.
• Strategic partnerships with labs that recognize these unique threats.

By bridging the gap between cybersecurity expertise and AI risk awareness, this approach could help secure critical systems more effectively.