Why Traditional Cybersecurity Won't "Fix" AI (hiddenlayer.com)

Industry instinct to "patch and test" is misguided for AI, argues HiddenLayer: unlike traditional software, AI systems fail as learned, distributed behaviors rather than discrete bugs. That means vulnerabilities—prompt injection, data poisoning, malicious realignment, inference attacks and inadvertent data leakage via RAG/MCP pipelines—are systemic and fluid. Built-in nondeterminism (context dependence, prior inputs, temperature) makes static test suites and one-off red teams insufficient; attackers can exploit the model’s ability to generalize and adapt faster than defenders can retrain or filter. Recent platform regressions (e.g., GPT-4o rollback, early GPT-5 behavior shifts) illustrate how updates themselves reshape threats. The path forward is not abandoning classic controls but fusing them with AI-native defenses: security-aware models, risk-reduction guardrails, deterministic policy enforcement, and continuous detection/response. Practically this means organization-wide AI discovery, supply-chain integrity for datasets and model artifacts, adaptive red teaming that probes across contexts and randomness, and runtime monitoring that tracks provenance, context windows, and model reasoning. Treat security as a continual lifecycle—detect, contain, recover in real time—because you can’t “patch” a capability spread across billions of parameters. HiddenLayer’s framework emphasizes those four pillars to move from protecting code to protecting adaptive capability.