DevSecOps FAQ: AI for Software Development Security
Security scanners find the easy stuff. A hardcoded API key, an outdated dependency with a CVE, SQL injection patterns. But the real vulnerabilities? Those live in the architecture.
I've watched teams run clean security scans while shipping systems where user input flows directly into admin functions, where authentication bypasses exist across microservice boundaries, where PII gets logged to third-party services. The scanners missed everything because they don't understand context.
AI-powered security tools promise to change this. They're supposed to understand code semantically, trace data flows, reason about architectural decisions. But most still operate like fancy pattern matchers. They need something they rarely get: a complete picture of your codebase.
What DevSecOps problems can AI actually solve?
, definitely. AI excels at finding variations of known vulnerabilities that traditional regex-based scanners miss. It can spot SQL injection attempts even when developers use creative string concatenation. It catches insecure deserialization across different frameworks.
Cross-file dependency analysis, which is where traditional SAST tools fail hard. AI can follow a variable from user input through ten files and five function calls to figure out if it ever gets sanitized. This matters because most real vulnerabilities span multiple files.
Configuration drift detection between what your security policies say and what your code actually does. You document that all API endpoints require authentication, but AI can verify whether your implementation actually enforces it everywhere.
Here's what AI struggles with: understanding business logic vulnerabilities. No amount of pattern matching catches "user A can access user B's data because we pass user IDs in URL parameters and only check session validity, not ownership." These require understanding what the code is supposed to do, not just what it does.
How does AI-powered security scanning differ from traditional SAST?
Traditional SAST tools run on rules. Lots of rules. Really specific rules. If your code matches pattern X in context Y with conditions Z, flag it. Fast, deterministic, and blind to anything the rule authors didn't think of.
AI-based scanners build a semantic model of your code. They understand that userInput, request.params['id'], and $_GET['userId'] are all the same concept: untrusted data entering your system. They can reason about data flow without explicit rules for every framework and language.
The practical difference: Traditional scanners produce mountains of false positives because they can't understand context. AI scanners produce fewer, higher-confidence findings because they trace actual execution paths.
But here's the catch. Both approaches fail without architectural context. They analyze files, maybe modules, rarely entire systems. A microservice that looks secure in isolation might expose your entire database when you understand how it interacts with your API gateway and authentication service.
This is where something like Glue becomes relevant. When security tools can query "show me all paths where user input reaches database queries" across your entire codebase—not just within a single service—you catch the architectural vulnerabilities. The ones that span repositories, cross service boundaries, exist in the gaps between what different teams built.
Can AI detect zero-day vulnerabilities in code?
Not really, despite what vendors claim. AI can detect patterns that look suspicious, which occasionally catches zero-days before they're named. But it's not magic.
What AI does well: Find code that violates security principles even without matching known vulnerability patterns. Untrusted data flowing into sensitive operations. Privilege escalation paths. Race conditions in authentication flows.
A concrete example: A team built a feature flag system where flags got cached per-user. The caching logic was fine. The flag evaluation was fine. But AI analysis spotted that admin users could modify flags for other users without cache invalidation, creating a time window where regular users inherited admin privileges. No CVE for that. No signature. Just code that violated the principle of least privilege in a subtle way.
The limitation? AI finds anomalies, not intentions. It might flag perfectly safe code that looks weird, while missing dangerous code that looks normal. You need humans who understand your threat model to triage.
Should we replace security engineers with AI?
Absolutely not. Replace the grunt work, not the expertise.
AI should scan every commit automatically. It should flag suspicious patterns. It should trace data flows and map attack surfaces. This frees security engineers from reviewing thousands of PRs looking for obvious mistakes.
What humans should focus on: Threat modeling. Understanding attacker motivations. Evaluating business logic vulnerabilities. Designing security architectures. Making trade-offs between security and functionality.
I talked to a security engineer at a fintech company who described their workflow. AI scanners run on every PR, auto-approving clean code, flagging suspicious patterns. Security engineers review only the flags, spending time on complex cases. They also run weekly architectural reviews where they use tools that map code ownership, complexity, and change patterns to identify high-risk areas.
They found that 20% of their codebase accounted for 80% of their security findings. Not because of bad code, but because those modules handled sensitive operations: authentication, authorization, payment processing. AI helped them focus, but human judgment determined where to invest security effort.
How do we handle false positives from AI security tools?
You tune them, just like traditional scanners. But the tuning process looks different.
Traditional scanners: Disable noisy rules, adjust severity thresholds, add exceptions to your config file. You're tweaking predetermined patterns.
AI scanners: Provide feedback on false positives so the model learns your codebase patterns. Tag findings as "expected behavior" with justification. The AI adjusts its understanding of what's normal for your system.
Better approach: Give AI more context. Most false positives happen because the scanner doesn't understand your architecture. That authentication bypass it flagged? Not a bypass—there's an API gateway doing auth before traffic reaches that service. The scanner doesn't know that because it only sees one repository.
Glue's approach to this problem is interesting. By indexing your entire codebase with architectural context, it can answer questions like "does this endpoint have authentication?" by checking not just the endpoint code, but the middleware, the API gateway config, the service mesh policies. Security scanners that integrate with this kind of code intelligence produce dramatically fewer false positives because they understand the full picture.
What about AI generating insecure code?
This is already happening. Copilot, ChatGPT, and similar tools confidently suggest vulnerable code. They trained on public repositories, many containing security flaws.
Example: Ask AI to generate authentication code, you'll often get JWTs without signature verification, passwords hashed with MD5, session tokens in localStorage. The AI learned these patterns from real code. Bad real code.
The solution isn't avoiding AI code generation—it's too useful. The solution is scanning AI-generated code more aggressively than human-written code. Treat it as untrusted input.
Some teams enforce a policy: Any AI-generated code must pass security review before merging, even for senior engineers. The PR template has a checkbox: "Contains AI-generated code" that triggers additional checks.
Others use AI to check AI. Run generated code through multiple security scanners. Have a second AI model trained specifically on secure coding patterns review the first AI's output. It's weird, but it works.
Can AI help with security documentation?
Yes, and this might be its most underrated security application. Documentation drift kills security programs. Your threat model document says one thing, your code does another, your runbooks describe a third thing.
AI can generate security documentation from code automatically. It can document authentication flows by tracing how your code validates tokens. It can map data flows showing where PII gets processed. It can maintain an up-to-date inventory of dependencies and their known vulnerabilities.
More importantly, AI can spot documentation-reality gaps. Your security policy says all API endpoints require authentication. AI can verify whether that's true and flag exceptions. Your architecture diagrams show user data staying in-region. AI can trace database calls and flag any that cross boundaries.
I've seen teams use Glue specifically for this. Because it maintains a semantic understanding of the entire codebase, it can generate documentation that stays current with code changes. When someone refactors authentication logic, the security documentation updates automatically. When a new service gets deployed, it appears in the architecture diagrams with its security context—what it accesses, what accesses it, where sensitive data flows.
What's the future of AI in DevSecOps?
The trend is clear: More context, less noise. Security tools will get better at understanding your entire system, not just individual files or services. They'll integrate with your development workflow, your documentation, your infrastructure-as-code, your observability stack.
We'll see security tools that can reason about your threat model and map it to your actual implementation. "You said credential theft is your top risk—here are the code paths where credentials are most vulnerable based on complexity, change frequency, and team expertise."
The winning tools won't be the ones with the fanciest AI models. They'll be the ones with the best architectural understanding of your codebase. Pattern matching is commoditized. Context is competitive advantage.
But here's my prediction: The security tools that succeed will be the ones that augment human decision-making rather than trying to replace it. AI that makes security engineers 10x more effective beats AI that tries to automate them away.
Security is fundamentally about trust boundaries, threat models, and acceptable risk. Those are human decisions. AI's job is to make those decisions informed by accurate, complete information about what your code actually does.