Cybersecurity Engineer's Guide to AI Workflows and Risks

AI coding tools are making teams faster. That part is real. The problem is that security maturity is not keeping pace with the acceleration. More AI-generated code is shipping before review practices have adapted. Issue #9 from Gradient Push breaks down the four-part pipeline teams need if they want AI-assisted development without quietly increasing risk. https://lnkd.in/eHUbxCQK #AICoding #AppSec #DevSecOps #SoftwareEngineering

The Code Doesn’t Care Who Wrote It: Why Context, Not AI Fear, Will Define Modern Application Security  AI has already arrived in the software development lifecycle; not as a pilot program or controlled experiment, but as an everyday reality. Developers are using AI coding assistants to generate functions, refactor modules, review pull requests, and accelerate delivery, often in direct tension with corporate policies meant to limit or control that use.  While it’s tempting to consider this some kind of ‘Shadow AI’ or ‘Governance Failure’, it is a signal of things to come in this brave new world of AI-accelerated software engineering. ...

🫵 Your AI coding agent just became an attack vector❗ Novee's research team found a high-severity vulnerability in Cursor IDE -now officially CVE-2026-26268. The attack? A developer clones a repo, something they do constantly and attacker code runs silently on their machine. No clicks. No warnings. Just a routine action turned into a full compromise. The scary part: the underlying behavior was already known. Nobody connected the dots until now. That's the difference between a scanner and a team that thinks like an attacker🔑 https://lnkd.in/ejsQP5UK

The speed versus security tradeoff with AI coding tools still needs some room to breathe. The real story is less about the generic warning and more about what the actual data shows once teams start shipping at scale. The velocity argument basically won, heck, we at Fencer are a part of the velocity argument given what we're shipping. What is still underappreciated is what the security picture actually looks like after that decision. The numbers are not pretty. Research shows AI-generated code has roughly 2.74 times the vulnerability rate of human-written code, Georgia Institute of Technology (shoutout to my alma mater!) spotted 35 CVEs directly tied to AI coding assistants in just one month this spring, and OWASP even added a whole new category for it last year. What we have been seeing across engineering teams is that the error patterns change before the review process catches up. I talked a founder about just this last week when he said they were "swimming in an insurmountable amount of bugs." I'm not saying don't use AI tools for coding, but there is a question on whether the review system you built for 20 PRs a week was ever meant to handle 50, 100 or even 200 and whether the checks that worked for human code are actually catching what AI code tends to produce. https://lnkd.in/e3k9v_Cs

The main thing with AI code is that it's very easy to produce. Even if the vulnerability rate was lower than human written code, the fact that we produce 10x or even 100x the amount of code in the same time frame means that the security implications are way higher than just 2.7x factor would lead us to believe.

ReGrade 3: Guardrails for AI-Generated Code AI coding tools produce 1.7x more bugs than human code. 2.74x more XSS vulnerabilities. 1.88x more improper password handling. That's from a CodeRabbit analysis of 470 GitHub repos — not a guess, not a prediction, a measurement. And the productivity story isn't what we thought either. A controlled study from METR found developers using AI tools were actually 19% slower — while believing they were 24% faster. Teams are shipping more code. Bigger PRs. Longer reviews. More bugs. The bottleneck isn't generation. It's validation. ReGrade 3 closes that gap. Record real API traffic against your trusted version. Replay it against your working copy. Compare every response field by field. Any behavioral change gets flagged automatically. Because ReGrade functions as an MCP server, your AI coding agent connects directly. The workflow becomes a closed loop: the agent writes code, ReGrade catches regressions at the network layer, and feeds structured diffs back. The agent self-corrects. No human in the middle. Your tests validate what you expect. ReGrade surfaces what you don't. https://lnkd.in/gPCH4kvz #ReGrade3 #AIcoding #DevSecOps #APISecurity #MCP #NCAST #Curtail

The stat that gets me is developers thinking they were 24% faster while actually being 19% slower. That's not a tooling problem — that's a perception gap. And you can't fix a perception gap with more code review. You fix it with deterministic behavioral comparison. ReGrade doesn't care how fast you shipped. It tells you what actually changed.

ReGrade 3: Deterministic Guardrails for AI-Generated Code Every AI coding tool on the market helps you write code faster. None of them tell you what that code broke. ReGrade 3 does. AI-generated code is probabilistic by nature. Every suggestion is a best guess — and even great guesses introduce subtle behavioral changes that are invisible in code review. ReGrade 3 provides deterministic guardrails for probabilistic output. It doesn't guess whether the new version behaves correctly — it observes and compares actual network behavior, response by response. Record real API traffic against your trusted version. Replay it against your release candidate. Compare every response field by field. Bugs, security anomalies, missing headers, behavioral drift — anything that changed gets surfaced automatically. No test scripts. No mocks. No SDK. Every API call becomes a test case. Drop ReGrade into your CI pipeline and every merge request gets an automatic behavioral regression report — before code hits main. No more merging blind and hoping your integration tests caught everything. If something changed, you know exactly what and where, right in the MR comments. ReGrade 3 also functions as an MCP server, so your AI coding agents connect directly. The workflow becomes a closed loop: your agent generates code, ReGrade detects regressions at the network layer, and feeds structured diffs back. The agent self-corrects. No human in the middle. This matters beyond new features. Teams refactoring legacy C/C++ into memory-safe languages like Rust can eliminate up to 70% of security vulnerabilities — but only if the rewrite doesn't change behavior. ReGrade gives you that proof automatically, field by field, across every API surface. In benchmarks: 3.2x faster debugging. 71% fewer tokens. 96% of deltas traced to root cause. The age of writing tests to validate AI-generated code is over. The age of observing behavior is here. ReGrade 3 is available now: https://lnkd.in/gP3d_uqG #ReGrade3 #APISecurity #DevSecOps #AIcoding #MCP #NCAST #Curtail

Deterministic Guardrails for AI-Generated Code is a method to ensure that your applications perform as intended without unintended consequences. Take a look.

This aged well. Anthropic just announced Claude Mythos Preview can autonomously discover and exploit zero-day vulnerabilities at scale — and they consider it too dangerous for public release. If AI can now weaponize the regressions your team didn't catch, deterministic behavioral guardrails aren't optional anymore. They're the last line of defense. Read more about Claude Mythos at: https://lnkd.in/gEaJYd4f #ClaudeMythos #ProjectGlasswing #NCAST #AppSec #DevSecOps #APITesting #CICD #AICode #EngineeringLeadership #ReGrade #AIGovernance