Even after a profession spent on the forefront of AI analysis and improvement, I can say with confidence that we’re in an unprecedented second. Whereas AI is undoubtedly probably the most disruptive expertise of our era, on the planet of cybersecurity, hype doesn’t cease threats. Turning the immense promise of generative AI, deep studying, and machine studying into tangible safety outcomes requires extra than simply entry to new fashions; it calls for a disciplined and purposeful analysis philosophy. That is a vital part to Precision AI®.
Our philosophy rejects the normal, remoted “ivory tower” of educational analysis and company R&D. As a substitute, it’s relentlessly targeted on real-world outcomes which might be deeply embedded throughout the groups constructing the merchandise, safe by its design, and brazenly collaborative. This blueprint guides our work and is constructed upon 4 core rules that I consider are important for making AI work for safety.
1. Analysis within the trenches
This philosophy begins with a foundational choice about construction. As a substitute of isolating researchers, we embed them immediately inside our product organizations for a easy cause: In cybersecurity, proximity to the place the place safety issues are solved is all the things.
This construction ensures our researchers are targeted on fixing real-world issues, not summary ones. Researchers sit alongside the engineers who construct our merchandise and the product managers who stay and breathe our prospects’ challenges. This proximity makes the switch of expertise seamless and natural. It fosters a relentless dialogue that ensures our long-term, high-risk analysis initiatives stay grounded in what’s going to finally make our prospects safer.
2. Higher safety outcomes: The one metric that issues
Many analysis organizations measure success by the variety of educational papers they publish. We don’t. Our major metric is the tangible enchancment our analysis delivers to our merchandise and, by extension, to our prospects’ safety.
This focus has two vital implications. First, it implies that we practice and consider our fashions on real-world safety system knowledge, not on sanitized “toy issues.” This ensures our AI is efficient within the advanced, messy actuality of a stay safety atmosphere. Second, it provides our groups the liberty to fail. We encourage a “fail-fast” mentality, enabling us to rapidly discard concepts that don’t present promise and double down on those who do, with out the stress of a publication quota. Our purpose is to construct a portfolio of confirmed, efficient AI for safety, to not construct a library of papers.
3. Safety for AI, not simply AI for safety
Because the chief in cybersecurity, we’ve a twin duty: to construct AI that promotes safety, and to make sure the AI we — and our prospects — construct is itself safe. Our prospects entrust us with their most delicate system knowledge, and defending it’s our highest precedence.
This precept extends to our total analysis operation. Our fashions are developed in extremely safe environments, protected by our personal best-in-class safety merchandise and secure-by-design frameworks. We meticulously vet our AI safety and shield in opposition to mannequin theft, immediate injections, and different rising threats. This may increasingly appear apparent, however safe AI is inconceivable with out first having a robust understanding of AI safety. Our dedication to this precept extends past our personal partitions; it’s the core of our promise to our prospects. The identical safety platform that protects our analysis is the one we provide to the business, guaranteeing everybody can profit from the teachings we be taught on the frontlines of AI innovation.
4. Create a neighborhood, not a fortress
Lastly, we consider the perfect concepts come from collaboration. We actively keep away from a “not invented right here” mentality. Our groups are empowered to leverage the perfect improvements from the broader analysis neighborhood. These improvements embrace utilizing LLM coding brokers to make our personal researchers extra productive and producing artificial knowledge to make our fashions extra sturdy.
We’re dedicated to being energetic members within the world dialog. We encourage our researchers to attend conferences, set up workshops, and repeatedly be taught from what others have completed. Progress on this area is a collective effort, and our purpose is to contribute to and profit from the shared data of the whole ecosystem.
A safer, safe future
In the end, these rules create a analysis engine that’s each modern and accountable. It’s an method designed to show cutting-edge science into real-world safety, guaranteeing that the facility of Precision AI delivers on its most necessary promise: a safer digital future for everybody.
To see these rules in motion and discover our deep analysis into securing this subsequent wave, learn our full paper, Attaining a Safe AI Agent Ecosystem.
