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As artificial intelligence transforms software development and global regulatory frameworks tighten, organizations face a defining moment: adapt or fall behind. Security is no longer just a best practice — it’s a foundational requirement for survival and success in the AI era. In a conversation with Jason Schmitt, CEO of Black Duck, the core message is clear: the balance between innovation and compliance has fundamentally shifted. Software must now be secure by design, scalable by default, and compliant by necessity. This shift requires rethinking not only how applications are built, but how security is embedded from the boardroom to the developer’s keyboard.
Let’s unpack the key takeaways from
Summary: Navigating the AI-Regulated Future of Application Security
AI and Regulations are the twin forces reshaping software: AI is accelerating development but also increasing risks, while regulations enforce accountability, transparency, and compliance.
Software security is no longer just a CISO concern: It has become a board-level issue, with major implications for governance and strategic planning.
Speed vs. security tradeoffs are obsolete: Organizations must eliminate compromises and achieve security that scales without slowing innovation.
Security must operate at “True Scale”: That includes speed, accuracy, volume, and compliance — all simultaneously, across varied development environments.
Four pillars of scale redefined:
Speed at Scale: Rapid development and deployment must not sacrifice security.
Accuracy at Scale: Precision is critical across different applications and environments.
Volume at Scale: Secure everything — from thousands of microservices to vast data flows.
Compliance at Scale: Stay current with all global and industry-specific regulations.
The role of business leaders has changed: Boards and C-levels must actively invest in scalable security infrastructure.
Key recommendations for organizations:
1. Elevate application security to a strategic priority.
2. Prepare systems for AI-scale operations.
- Stay ahead of regulatory curves to avoid disruptions.
- Secure every phase and element of the SDLC, including open-source and AI-generated code.
5. Embrace decision-making models that remove speed-security tradeoffs.
- Implement tools that combine accuracy with ease of use for developers.
- Start securing code wherever it lives — then expand.
Security tools must evolve too: They should integrate seamlessly, be developer-friendly, and cover the entire codebase — from proprietary to AI-generated code.
Jason Schmitt’s insights underscore urgency: With his experience in both technical and executive roles, Schmitt sees the future as dependent on scale — scale in thinking, execution, and governance.
What Undercode Say: The Reality Behind Scalable Software Security
The convergence of AI and regulation presents both a challenge and an opportunity — but organizations that fail to adapt are exposing themselves to existential threats. Here’s a deeper analysis of the emerging dynamics and how they intersect with the real-world pressure developers and security teams face.
1.
AI is enabling rapid prototyping, automated testing, and intelligent code generation, but it’s also amplifying the complexity of threat vectors. Models like GitHub Copilot can unknowingly insert vulnerable code patterns. Meanwhile, AI-powered code refactoring can obscure vulnerabilities, making traditional scanning tools obsolete unless they are also AI-native.
2. Compliance is no longer an afterthought
Frameworks like the EU’s Cyber Resilience Act, the U.S. Executive Order on Improving the Nation’s Cybersecurity, and SBOM (Software Bill of Materials) requirements are forcing enterprises to rethink vendor selection, development workflows, and third-party risk. Compliance isn’t a paper trail — it’s becoming code-aware.
3. The myth of shift left is evolving
While DevSecOps and shifting security earlier in the SDLC are good practices, they must now be complemented by post-deployment, runtime protection. A holistic approach includes SAST, DAST, SCA, RASP, and SBOM generation — all tied into CI/CD pipelines that are AI-optimized.
4. Developer experience is a critical vector
Security that slows developers gets ignored. Tools must be frictionless, language-agnostic, and context-aware. GitOps, AI-based policy engines, and IDE-integrated vulnerability detection are now essential.
5. Scale is not just quantity —
True scale in security means being able to scale horizontally (across applications and teams) and vertically (across lifecycle stages and risk profiles). Black Duck’s emphasis on “speed, accuracy, volume, and compliance at scale” reflects a necessary paradigm shift.
6. Regulation-driven security will separate leaders from laggards
Boards must invest not only
References:
Reported By: www.darkreading.com
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