Building an AI-Ready AppSec Team in the Agentic Age: Skills and Training

According to a 2024 study on the rise of generative AI, 99% of development teams now use AI tools for code generation, yet 80% express concerns over the potential security risks introduced by AI-generated code. This paradox defines the new reality for application security leaders: The code is being written faster, but vulnerabilities are spreading wider.

To stay ahead, AppSec teams must evolve. The new generation of application security platforms includes AI capabilities with autonomous, intelligent agents that work alongside humans to identify, correlate, and remediate vulnerabilities at scale. 

To help security-minded developers stay ahead of the curve, we’ve broken down the skills, tools, and training required to build an AI-ready AppSec team that thrives alongside agentic AI.

Understanding the Agentic Age in AppSec

Agentic AI refers to systems composed of intelligent agents that act independently to achieve application security objectives. These AI agents go beyond following scripts by interpreting context, assessing risk, making decisions, and even suggesting remediations. Unlike traditional automation, which is rule-based and static, agentive AI is dynamic and adaptive.

Imagine a scenario where one agent scans source code for vulnerabilities while another correlates these findings with runtime behavior. A third agent might prioritize the most critical issues based on business impact, while yet another suggests remediations tailored to the developer’s coding language and framework. This collaborative network of autonomous agents represents a fundamental shift in how security teams interact with AI not just as a tool, but as a partner.

Since AI is now embedded across the AppSec lifecycle, from code generation to runtime monitoring, intelligent agents are enabling smarter, faster, and more scalable security processes.

AI-enhanced tools can scan source code for insecure patterns and explain findings in natural language. At runtime, machine learning models monitor application behavior to detect anomalies like data exfiltration or privilege abuse. AI also plays a growing role in discovering undocumented APIs, identifying vulnerable dependencies, and correlating data across different scanning methods to reduce false positives. Together, these capabilities form a more holistic, context-aware AppSec strategy.

Core Skills for an AI-Ready AppSec Team

To work effectively with AI-driven platforms, security professionals must develop skills in several core areas, and prompt engineering has emerged as a valuable practice for learning how to phrase questions or commands to extract the most relevant insights from AI systems. Equally important is AI and ML literacy for understanding how models function, where they can fail, and how to interpret their output with a critical eye.

Security professionals must also adopt a secure-by-design mindset that accounts for how AI-generated code can introduce new patterns of vulnerability. Tool proficiency is the final piece of the puzzle, ensuring team members can use and act on the insights provided by AI tools embedded in IDEs, CI/CD pipelines, and runtime environments.

Human Skills for Thriving in an AI-Augmented AppSec Environment

AI systems are powerful, but only when guided by capable human operators. AppSec professionals must develop both technical and soft skills to collaborate effectively with AI.

On the technical side, familiarity with AI fundamentals is key. Knowing how models are trained, what their limitations are, and how to craft effective prompts. Understanding the security context behind AI findings, such as the connection between flagged issues and CWE or OWASP categories, helps transform raw output into actionable insight.

Developers and AppSec professionals can build these AI-related technical skills through several targeted resources:

• Online courses: Many learning platforms offer foundational courses on AI, machine learning, and prompt engineering tailored for software engineers. Look for ones that cover model behavior, tokenization, and application in code review or security contexts.
• Hands-on with AI tools: Using AI-powered secure coding assistants inside your IDE gives real-time exposure to how AI thinks about and flags vulnerabilities.
• Developer documentation and blogs: Reading the technical docs of tools you use (e.g., Checkmarx, GitHub Copilot) will help you understand how their AI systems interpret and analyze code.
• Security communities and forums: Engage with developer-focused communities like to see real examples and discuss AI outputs with peers.

Soft skills are equally critical. Critical thinking enables practitioners to validate AI findings and identify when the system may have hallucinated or misunderstood the context. Strong communication skills help security teams translate AI-generated reports into actionable advice for developers and business stakeholders. A mindset of curiosity and ethical responsibility rounds out the profile of an AI-augmented security professional.

To gain these essential soft skills for working effectively with AI in AppSec, professionals can:

• Take structured online courses with a focus on critical thinking, communication, and ethics in technology. These skills can even translate beyond an AI context to level up your career in other areas.
• Participate in cybersecurity bootcamps or workshops that include real-world scenarios where communication and judgment are tested alongside technical skills.
• Join communities where professionals can engage in open discussions, review AI-generated findings, and refine their ability to explain security issues clearly.
• Practice through pair programming or peer code reviews, especially when using AI tools. This builds the habit of translating AI insights into actionable guidance.
• Read case studies and postmortems involving AI in security to understand where human interpretation added (or failed to add) value.

Choosing the Right AI Security Tools for Your Team

To fully benefit from AI, organizations must integrate it into their development pipelines with intentional design. The most effective AI platforms are those built around agent-based architectures. These systems use specialized agents for tasks such as code scanning, data correlation, and runtime behavior analysis, creating a layered and resilient defense model.

Human oversight remains essential. Guardrails should be implemented to monitor AI-driven decisions, especially those that affect code remediation or policy enforcement. And as with all security tooling, context is key. The best AI platforms correlate findings across multiple sources to highlight the most relevant and actionable risks, going beyond surfacing vulnerabilities.

Not all AI-powered security tools are created equal, though. AppSec leaders should prioritize platforms that reflect agentive AI principles with autonomous agents that perform specialized tasks, explain findings in human-readable language, and integrate seamlessly with existing development workflows.

Transparency and explainability are essential. Teams need tools that make it clear why a vulnerability was flagged, how it can be fixed, and what the broader security context is. Integration also matters. From the developer IDE to the CI/CD pipeline and ticketing systems, AI tools should fit naturally into the software development life cycle (SDLC).

Ultimately, the most secure AI solution is one that enhances collaboration, accelerates remediation, and builds trust across security and development teams.

Modern secure coding assistants are also reshaping how developers learn and implement security best practices. The Checkmarx AI Secure Coding Assistant (ASCA), for example, integrates directly with Visual Studio Code to provide real-time security insights.

To use tools like ASCA effectively, developers need to move beyond treating the assistant as a passive scanner. They should engage with it critically, understanding what is being flagged and why, and applying secure coding patterns that are both effective and AI-readable. This requires a combination of technical confidence, ongoing curiosity, and a willingness to learn from every coding session.

From Awareness to AI-Augmented Expertise

AI won’t replace AppSec teams, but it will elevate those that learn to collaborate with it. The future of secure development depends on building teams that blend technical skill with AI literacy, human oversight, and a willingness to learn.

Agentive AI is already here. The question is: is your team ready? Follow the link if you’re interested in the Seven Steps to Safely Use Generative AI in Application Security.