Secure Software Development LifeCycle (SDLC) for GenAI

As per sources, “78% of organizations report using AI in at least one business function as of 2025, showing dramatic uptake from previous years.” Enterprises are embedding GenAI into customer support, internal knowledge systems, CRMs, analytics platforms, and decision-support tools. While the innovation curve is steep, the risk curve is steeper. Many organizations are building GenAI features on top of a traditional software development lifecycle (SDLC) without accounting for the unique threat models introduced by probabilistic systems, natural language inputs, and autonomous outputs. Security teams are discovering that GenAI does not simply extend existing risks, but also reshapes them. Prompt injection, data exfiltration, and uncontrolled output propagation are not edge cases anymore; they have now become systemic concerns. To build reliable GenAI systems, security must be designed into the secure software development life cycle from the first architectural decision, and not as a post-deployment patch. This blog explores how a secure SDLC must evolve for GenAI, with a practical focus on real attack vectors, governance mechanisms, and review workflow design that aligns engineering velocity with enterprise-grade risk management. Why GenAI Changes the Security Conversation Traditional application security is deterministic. Inputs follow defined schemas, logic paths are predictable, and outputs can...

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