Let’s kick this off with a hot topic: Will AI replace cybersecurity analysts, DevOps engineers, or AppSec engineers?
It seems today that adapting to – and embracing – AI in AppSec is no longer optional. It’s true that AI is no longer a futuristic fantasy. It’s a vibrant reality shaking up every aspect of development and cybersecurity, but there is a tempest coming with both promise and peril: AI-generated code.
Those fancy new GenAI tools your developers have fallen head-over-heels for are rapidly becoming indispensable for productivity, but let’s get real: They’re also becoming an increasingly juicy target for attackers.
Let’s get back to the question at hand, though. If you ask me, then the answer is AI won’t, and can’t, replace expert level engineers and analysts anytime soon, but that doesn’t mean it isn’t already leveling them up. Let’s dive into what that means and how we can secure the entire AI-driven development lifecycle.
AI Code: Friend or Foe?
Here are the facts: Developers are embracing AI-generated code because it boosts productivity. These Large Language Models (LLMs) excel at writing impressive snippets of code, completing boilerplate tasks, refactoring and improving existing codebases, debugging and identifying errors, and making documentation slightly less soul-draining.
But there’s a catch: LLMs weren’t exactly schooled in secure coding best practices. That slick-looking code snippet? It might just be the cybersecurity equivalent of a beautiful yet structurally unsound bridge. It looks fantastic, but it could collapse spectacularly under attack.
AI also reflects biases and mistakes inherent in training data (public repositories), potentially propagating outdated, inefficient, or insecure practices. It also has a potential to hallucinate – make things up, including even inventing non-existent libraries. Unclear intellectual property and licensing risks stemming from code generation using proprietary or GPL-licensed open-source repositories also poses legal and licensing risks.
This may be AI 101, but developers can’t implicitly trust AI-generated code as sound and secure. That trust, without verification, is an open invitation to trouble. They need review, and human eyes are still non-negotiable.
Navigating AI Security Risks in AppSec
Generative AI introduces new attack vectors targeting not only your apps but also your AI tools themselves. Attackers can exploit vulnerabilities inherent in the AI ecosystem, poisoning training data or tricking AI code generators into spewing insecure or malicious code. The more dependent your workflows become on AI, the more urgent your need to secure those AI processes.
Yet, halting AI adoption altogether isn’t practical. Governance challenges arise, and executives see AI as essential for productivity. Fighting adoption is pointless. Instead, let’s set guardrails to encourage responsible use, train your developers to scrutinize AI-generated outputs, and, most importantly, integrate security scanning directly into your developer workflows.
Embracing AI in Application Security
AI poses risks, but it also plays a pivotal role in modern AppSec. The very technology creating vulnerabilities also equips us with potent new AI security tools to counteract threats.
Consider the power of AI secure coding assistants. By integrating intelligent scanning directly within the IDE, these tools identify vulnerabilities as code is written, before risky code even hits the repository. Real-time feedback on AI-generated and manually written code gives developers immediate insights, empowering them to fix vulnerabilities instantly. This approach shifts security left dramatically, catching mistakes long before they escalate into costly disasters in production.
Moreover, there’s AI query building, an unsung hero that leverages generative AI to construct and enhance custom security queries to enhance fidelity. Whether it’s Static Application Security Testing (SAST) or Infrastructure-as-Code (IaC), AI-powered query tools dramatically accelerate AppSec workflows. Developers and security analysts alike can write targeted, precise queries without spending hours buried in documentation, boosting efficiency and coverage in equal measure.
However, if you are going to use AI in AppSec, it is best used as an integral part of the software development lifecycle (SDLC) rather than just a tacked-on tool because of the need for unified risk visibility across the entire SDLC. Simply put, the stakes are too high to leave any stone unturned when evaluating AI. With a holistic approach to AI across multiple AppSec domains, there are fewer places for blindspots to hide.
Intelligent Remediation and AI Security Champions
Perhaps the most exciting area where AI shines in cybersecurity is remediation. Manual remediation is time consuming, inefficient, and slows down the whole development cycle. Developers dread it, AppSec teams hate nagging about it, and vulnerabilities linger far longer than they should. Unfortunately – and fortunately – it’s a necessary part of the process.
Enter AI-assisted remediation. Imagine not only pinpointing a security flaw but instantly receiving actionable, tailored suggestions for fixing it. AI remediation tools do exactly that: They analyze vulnerability findings from your SAST or IaC security scans and generate ready-to-use code snippets tailored to your specific issues. It’s like having an AppSec expert embedded within each developer’s IDE. There’s no need to worry about the AI making changes behind your back, either. AI remediation should never auto-commit changes. Rather, it provides suggested fixes that developers review and apply.
In practice, teams drastically cut their vulnerability backlog, improving security posture at unprecedented speed. Developers, armed with immediate solutions, actually grow their security awareness by seeing detailed explanations of the issues alongside code fixes. It’s security training disguised as productivity, exactly how AppSec pros like it.
Governance and Culture: Your Best AI Security Tools
While AI technology transforms cybersecurity tools and practices, it’s your culture and governance approach that ultimately determine success. AI is indeed a powerful partner, but it won’t replace cybersecurity teams. Instead, AI is just another tool that, when used correctly, enhances the team’s productivity. It’s always scanning, learning, and offering insights, but it still requires seasoned oversight.
The human role in AppSec isn’t diminished by AI; rather, it’s elevated. The job now involves orchestrating AI tools effectively, training teams, and evolving security programs to keep pace. Establish clear guidelines for AI use, embed AI security capabilities into your AppSec strategy, and ensure ongoing assessment and adjustment.
Incorporating AI in your AppSec program also requires maturity in governance. Carefully outline how your organization adopts and secures AI, from code generation to deployment. Define rules around which AI tools are acceptable, how outputs are vetted, and which security scanning mechanisms must accompany AI-generated code. Governance, when thoughtfully implemented, turns AI from potential liability into tangible strategic advantage.
Final Thoughts: Your AI-Enhanced Security Future
AI won’t replace your cybersecurity team, but it will reshape your workflows profoundly. Your task isn’t to fear AI but to harness it strategically. Deploy intelligent scanning, automate remediation with AI assistance, and establish clear governance around AI-generated code. Embrace AI confidently, understanding both its risks and rewards.
From code generation through testing and deployment, integrating AI responsibly creates safer, more efficient, and even more enjoyable AppSec experiences. Embrace it wisely, guide it carefully, and you’ll find your application security team not replaced, but transformed into a supercharged, AI-enhanced security powerhouse.If you’re sold on the value of AI in AppSec and how it can enhance your team, try checking out Checkmarx AI Security.