The speed of AI-assisted development is useful only when the resulting software remains understandable, testable and secure.
A symposium on AI-powered development demonstrated how tools such as GitHub Copilot can accelerate common coding tasks. It also showed the central risk: plausible code can contain vulnerabilities that are easy to miss when a team is moving quickly.
A convincing answer is not a verified answer
In one demonstration, an AI assistant helped build a coffee-shop application but introduced an injection vulnerability. A user could manipulate a query and change a product price. The assistant could also suggest a correction, but that did not remove the underlying lesson. The same system that produces a useful function can produce a dangerous one.
AI models optimise for likely output, not for the complete security context of an application. They may not know the organisation's threat model, data sensitivity, authentication boundaries or deployment environment. Developers remain responsible for those decisions.
Use generated code as a draft. Read it, explain it, test it and validate its dependencies before it becomes part of the product.
Put security inside the feedback loop
Snyk was presented as one way to identify vulnerable dependencies and risky code patterns during development. The key value is timing. Security feedback is more useful when it appears in the editor, command line and pull-request workflow than when it arrives shortly before release.
A practical AI-assisted workflow should combine several layers:
- Define secure coding guidance for the team and the tools it uses.
- Review generated changes with the same care as human-written code.
- Run automated tests, static analysis and dependency checks.
- Test inputs, permissions and failure paths that the happy path hides.
- Keep a human accountable for the final behaviour.
Speed needs engineering discipline
AI can remove repetitive work and help developers explore unfamiliar APIs. That is a genuine advantage. The mistake is treating faster output as evidence of correctness.
The best use of AI is not unsupervised code generation. It is a tighter loop between drafting, reasoning and verification. Tools such as Snyk strengthen that loop, but no scanner replaces judgment. Secure AI-assisted development begins when teams preserve ownership of every line they ship.