There are three fundamentally different approaches to securing AI-generated code. Understanding the differences matters.
Inject security guidelines into the AI's context before it writes code. Hope it listens.
Match code against human-curated rule databases. Miss anything that doesn't match a known pattern.
AI declares security semantics. A property lattice formally verifies them. No hope required.
See exactly how Acutis stacks up against specific tools.
Advisory guardrails vs. formal verification. Same problem, fundamentally different security guarantee.
Pattern matching vs. property lattice. Both have MCP servers — one runs rules, the other runs formal proofs.
The gold standard of semantic SAST — but it needs a full build and can't run in the AI generation loop.
"Secure at Inception" with ML-based scanning vs. formal verification with mathematical proofs. AI checking AI vs. math checking AI.