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Every board is asking some version of: “Are we safer or riskier with AI in engineering?” The answer depends on governance. This talk introduces a simple executive framework for AI use in development — one that preserves velocity while reducing risk from insecure patterns, license surprises, data leakage, and unreviewed changes moving faster than human oversight. We’ll explain why AI increases variance in code quality and why traditional controls (manual review, ticket-based approvals) don’t scale. Then we’ll outline guardrails that do: policy-as-code for what can ship, automated verification for what’s been pulled in, and clear accountability for “human-in-the-loop” decisions. Importantly, we’ll position AI as a force multiplier for both attackers and defenders — and show how mature financial services organizations respond: they don’t ban AI; they create measurable, enforceable standards for trust. The outcome: faster delivery with fewer surprises in production and compliance.
