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    DevSecAIOps Security for AI-assisted development

    A practical framework to cut risk, speed up decisions and harden the SSDLC when AI is in the loop

    A whitepaper on aligning security and the SSDLC with AI-assisted software delivery.

    It covers the numbers, the risks and a DevSecAIOps roadmap for engineering and security teams.

    Download whitepaper What’s inside

    Context

    AI-assisted development is moving faster than traditional security can keep up

    These figures capture the shift: more AI-generated code, a wider attack surface and little room to rely on manual review or legacy tooling alone.

    46%

    Share of code written with GitHub Copilot among active users (up to 61% in Java)

    86%

    XSS failure rate in AI-generated Java (the highest single vulnerability observed)

    45%

    AI-generated code samples with OWASP Top 10 issues — Veracode 2025 (100+ LLMs)

    90%

    Enterprise engineers who will use AI coding assistants by 2028 (Gartner)

    2.74×

    More defects in AI-generated code than in human-written code

    <5%

    False positives with AI-native tools (versus 30–60% for legacy SAST such as Checkmarx / Veracode)

    “The spec is the new security perimeter.”

    Spec-driven development: the biggest win is not scanning after the fact—it is defining security requirements before AI-generated code lands in your repos.

    Structure

    What you’ll find

    Six themes that unpack the problem, the change in approach and how to put it into practice.

    1. 01

      The velocity paradox

      AI ships code faster than traditional controls can review it.

    2. 02

      Why legacy security no longer cuts it

      This is not a marginal tweak: you need a new architecture, not just more scanners.

    3. 03

      The AI-native security shift

      New platforms combine context, automation and code-to-cloud visibility.

    4. 04

      Spec-driven security

      Defining requirements before code is generated cuts risk and speeds validation.

    5. 05

      Transition playbook

      A four-phase roadmap for evolving your practice without grinding delivery to a halt.

    6. 06

      AI Security Studios

      The closing section ties the framework to real delivery and day-to-day operations.

    Plain Concepts

    AI Security Studios

    Specialist services to secure AI systems: governance, architecture, AI-aware SSDLC and runtime defence.

    • AI security architecture Design and governance for safe, compliant and resilient use of AI.
    • Offensive AI Exercises and testing augmented with AI to harden defences before attackers do.
    • Agentic security Operations with AI-assisted triage, enrichment, correlation and orchestration.
    • DevSecAIOps Secure code-to-cloud lifecycle: AI-aware SSDLC with dynamic risk prioritisation in the delivery pipeline.
    • 19+ years of technology innovation
    • 100+ complex AI deployments
    AI Security Studios Download whitepaper