Cyber Daily Report

Bala Thripura Akasam--securityboulevard.com
published date: 2025-11-26 00:00:00 UTC

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Artificial intelligence is no longer a novelty in software development. It is now writing code that runs in production systems. Tools like GitHub Copilot, ChatGPT and Amazon CodeWhisperer accelerate development cycles and enhance productivity. However, they also add a new layer of application-level risk that many organizations have yet to grasp. As companies scale up AI-assisted coding, <a href="https://securityboulevard.com/2025/11/securing-ai-generated-code-what-does-it-look-like-in-practice/" target="_blank" rel="noopener">they face a new security challenge</a>: Ensuring that machine-generated code meets the same or higher security standards as code written by humans. <h3 aria-level="1">The New Reality: AI as a Developer </h3>Application security programs were built around predictable, human-driven development. We train developers on secure coding, implement Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST) in CI/CD, and rely on manual reviews to catch what tools miss. But AI shifts the entire landscape. Large Language Models pull from massive codebases, including open-source projects that may use outdated or insecure patterns. When developers rely on AI to generate code, they often end up with snippets that look clean and correct but overlook things like input validation, proper logging, or authorization checks. Two key issues make this especially risky: <ol><li>Unknown provenance: We can’t pinpoint where the code came from or confirm whether it aligns with company policies. </li><li>Invisible vulnerabilities. Many generated snippets avoid detection by traditional scanners because they look to be semantically correct. </li></ol>Put together, this creates a new kind of “shadow code”, which is code that runs well and seems fine, but can introduce real security risks. <h3 aria-level="1">Why Traditional Application Security Isn’t Enough </h3>Most security programs still rely on static and dynamic scanners that look for familiar patterns. However, AI-generated vulnerabilities are often related to logic, rather than syntax. You might get an access-control check that appears fine at first glance but validates the wrong role, or code that completely forgets to include audit logging. Traditional scanners also assume that a human wrote the code and adhered to common design principles. AI models can merge pieces of unrelated patterns, leading to new vulnerabilities that signature-based tools usually miss. The result is code that compiles cleanly, passes every automated test and still has serious, exploitable gaps. <h3 aria-level="1">Three Priorities for Application Security Leaders </h3>1. Define “AI Code Governance.” Organizations need to establish a clear policy for using AI in development. Developers must tag AI-generated code in commits, document the tools used and ensure a proper manual review before merging into main branches. Also, it is crucial to set up a simple approval process. This could mean open-source license reviews for any AI-assisted contribution. This guarantees traceability and accountability in case a future incident comes up. 2. Expand Testing to Include Behavior and Context Traditional SAST and DAST are no longer sufficient on their own. Teams need to layer in things like fuzz testing, runtime instrumentation and tools that actually understand how business logic works to catch the kinds of mistakes AI can introduce. There’s also a new wave of tools explicitly built to spot “AI-style vulnerabilities”. These include things like weak randomness or sloppy data validation. And if your vendor doesn’t offer that yet, it may be worth building some internal models trained on real examples so you can start catching those issues early. 3. Train Developers on Secure AI Usage The most effective safeguard is still human oversight. Therefore, developers should be trained to treat anything produced by AI as untrusted until they’ve reviewed it themselves. They also need to be careful not to feed sensitive or proprietary information into public tools, and to double-check any security-sensitive logic before it goes live. These habits should be integrated into your existing secure coding training and built into the SDLC so they become part of the typical workflow. <h3 aria-level="1">A New Mindset: From Shift-Left to Think-Wide </h3>“Shift-Left” has been a common motto in secure development. What it emphasizes is catching issues earlier. However, in the era of AI, security must also move to a new motto of: “Think-Wide.” Application Security now extends beyond developers to include data scientists, model owners, and compliance teams. Therefore, security professionals need to consider these key questions: What data trained this model? Does it incorporate insecure code from public sources? Can we explain its logic if a vulnerability arises? This collaboration across different roles showcases the importance of a cultural change in how we view software assurance. <h3 aria-level="1">Turning AI into an Ally </h3>Despite all the risks, AI can also help improve Application Security. The same models that create insecure code can be trained to produce test cases, examine vulnerabilities, and give suggestions for secure coding fixes on a large scale. Proactive teams are already introducing “security copilots” that check code and suggest fixes automatically. If handled responsibly, AI could reduce repetitive tasks and help enhance coverage across complex application portfolios. <h3 aria-level="1">Conclusion </h3>AI-generated code is already part of the enterprise landscape. It’s not going away. The organizations that thrive will be those that adopt this new development model before it becomes the standard. Application security managers must lead this change by defining governance, improving testing and empowering developers to use AI responsibly. In the years ahead, Application Security will not just be about protecting human code. 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