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AI models rely on Humans, both at deployment and development to tell them what to do. They lack the capacity - unlike us - to understand things contextually. To them, all is probabilistic.
Much like a dog, even though there are several methods to train them we most often use reinforcement training. Positive or negative stimuli is used to teach it (dog or AI) which behaviours are desired (good) or undesired (bad). Imagine now that, behind your back, someone's been mistraining your dog - worse even, they've also tampered with its training grounds.
It's exactly this Blindspot that attackers will take advantage of as though your model can't see in the traditional sense, it can detect these changes and it change its interpretation of what it "sees".
What happens then? When the "wrong" behaviours are the ones being reinforced? Your dog might learn to bite the mailman instead of guarding the perimeter;
Average breach cost was US $4.8 M;
73 % of enterprises suffered at least one AI-related security incident in the last 12 months;
Training-data poisoning now accounts for 23 % of all AI-security incidents, #2 after prompt-injection;
We are working toward helping the sector secure itself. Currently seeking to validate the market while further developing our Proof-of-Concept, that boasts a 98% accuracy rate, into a Full stack Integrity Platform for AI.
Open to meeting with Investors about accelerating our process. Wanting to anticipate markets needs so that we can better position ourselves to offer a premium solution.
Open to matching companies interested in becoming Early Adopters and signing Letters of Intention or trying our product at a discounted rate in exchange for Detailed Feedback.
We're researching and developing Label distribution checks. We want to increase supported model types (text, audio, etc), create a signature library of known poisoning campaigns, and clear dashboards for confusion matrices, PR curves and attack impact.