Building an AI-Ready Cybersecurity Team

AI is reshaping cybersecurity faster than most teams can adapt. Nearly half of IT decision-makers say lack of AI expertise is now their biggest implementation challenge, yet 97% of organizations are already using or planning to deploy AI-based security solutions. The gap between AI adoption and AI capability is widening, and it’s creating real risk.

Security leaders face a dual challenge: they need cybersecurity teams that can leverage AI tools effectively while maintaining the foundational skills that make security professionals effective in the first place. Rushing to adopt AI without ensuring your team has core competencies creates risk. Ignoring AI entirely leaves you vulnerable to adversaries who won’t wait for you to catch up.

This article provides a practical framework for building an AI ready cybersecurity team. We’ll cover how to assess your team’s current capabilities, where to invest in AI-specific training, and why foundational hands-on skills remain essential in an AI-augmented security environment. OffSec’s approach to training emphasizes practical, hands-on skill development that builds the problem-solving capabilities security professionals need, capabilities that AI tools can enhance but not replace.

An AI-ready team isn’t just a team that uses AI tools. It’s a team with the skills to use them effectively, securely, and critically. This means developing capabilities across two dimensions: the ability to leverage AI for defensive operations, and the ability to secure AI systems and defend against AI-enabled attacks. The goal is AI augmentation, not AI dependence.

Artificial Intelligence was not a top skill requirement two years ago, but now 41% of respondents cite it as a critical skill, ranking it first for the second consecutive year, followed by cloud security at 36%. But AI skills alone aren’t sufficient. According to the ISC2 2025 Cybersecurity Workforce Study, 73% of professionals believe AI will create more specialized security skills, not fewer. Teams need professionals who can think critically about AI outputs, understand their limitations, and apply human judgment where it matters most.

Foundational skills still matter because AI tools are force multipliers, they amplify the capabilities of skilled professionals. Without foundational skills, teams can’t evaluate AI-generated outputs, recognize when AI is wrong, or operate effectively when tools fail. Problem-solving, persistence, and adversarial thinking remain human advantages that AI cannot replicate. The “Try Harder” methodology builds exactly these non-automatable capabilities.

The key insight here is straightforward: AI-ready doesn’t mean replacing traditional skills with AI skills. It means layering AI capabilities on top of a strong foundation of hands-on security competencies.

You can’t build an effective training program without understanding where gaps exist. The 2025 ISC2 study found that 88% of respondents have experienced at least one significant cybersecurity consequence because of a skills deficiency, with 69% experiencing more than one. Assessment helps prevent this. Different roles require different AI capabilities, and a one-size-fits-all approach wastes resources.

Start by mapping roles to AI requirements. SOC analysts need proficiency in AI-assisted threat detection, recognizing AI-generated threats, and using AI-enabled SIEM/XDR platforms. Penetration testers need to understand how attackers target AI systems, test AI systems for vulnerabilities, and work with frameworks like the OWASP LLM Top 10. Security engineers need skills in securing AI integrations, understanding prompt injection risks, and AI model security. GRC professionals need expertise in AI governance, risk assessment for AI deployments, and emerging regulations.

Before assessing AI readiness, ensure your team has core competencies. Can they think like an attacker? Do they have hands-on experience with real systems? Gaps in fundamentals will undermine AI tool effectiveness.

Once foundational skills are confirmed, identify AI-specific gaps. Evaluate your team’s understanding of how AI-enabled attacks work, including deepfakes, AI-generated phishing, and automated reconnaissance. Assess familiarity with AI-specific vulnerability classes like MITRE ATLAS and OWASP LLM Top 10, experience securing or assessing machine learning systems, and the ability to critically evaluate AI tool outputs.

Finally, distinguish between “nice to have” and “critical” based on your organization’s AI exposure. If you’re deploying LLMs internally, securing them is critical. If AI-enabled SOC tools are central to operations, proficiency is essential. Assessment should reveal both where AI training is needed and where foundational skills need strengthening before AI training will be effective.

Effective AI security training follows several principles that determine whether your investment produces real capability or just checkbox compliance.

AI capabilities are built on core security competencies, so ensure team members have hands-on experience before layering AI skills. Teams without foundational skills will misuse or over-trust AI tools.

Reading about cyber threats doesn’t build detection capability, and certifications validated through multiple-choice exams don’t prove practical competence. Teams need lab environments, realistic scenarios, and practice under pressure. The Fortinet 2025 Global Skills Gap Report found that 86% of organizations experienced at least one cyber breach in 2024, with 28% reporting five or more, and theoretical knowledge clearly isn’t enough.

Understanding how attackers use AI makes defenders more effective. Red team knowledge of AI attack techniques improves blue team detection.

Generic “AI for cybersecurity” training wastes resources because SOC analysts need different skills than penetration testers. Customize development paths based on job responsibilities and the specific AI security tools and threats each role encounters.

AI capabilities evolve rapidly, and point-in-time training becomes outdated quickly. The global cybersecurity workforce gap has reached 4.76 million unfilled positions, and 70% of cybersecurity professionals are already pursuing AI qualifications.

Don’t sacrifice core training for AI training. The most effective teams have both strong fundamentals and AI capabilities. Consider a development path that establishes fundamentals first through PEN-200, then adds AI-specific skills through specialized training. The Fortinet report found that 87% of professionals expect AI to enhance their roles, but enhancement requires a role worth enhancing.

Traditional hiring focused on years of experience and certifications. AI-ready hiring should also evaluate adaptability, critical thinking, and learning agility. Look for candidates who can grow with the technology, not just those who know today’s tools.

Prioritize certifications that require practical demonstration, like the OSCP, which carry more weight than multiple-choice exams. The Fortinet report found that 89% of IT decision-makers prefer certified candidates, but certification type matters. Look for evidence of lab work, CTF participation, or practical projects that demonstrate real capability. The benefits of a fully certified cybersecurity team extend beyond individual skill validation to improved team cohesion and standardized capabilities.

AI changes rapidly, and problem-solving skills transfer across tool changes. The “Try Harder” mentality, persistence and creative thinking under pressure, determines who will use AI effectively. These capabilities can’t be replaced by AI and separate professionals who leverage tools from those who depend on them.

Can candidates identify when AI outputs are wrong or biased? Do they understand AI limitations, not just capabilities? Teams need humans who question AI, not humans who blindly trust it. This skeptical but constructive approach to AI solutions is essential for security work.

AI skills have a short half-life, so prioritize candidates who demonstrate ongoing self-development. Look for engagement with professional communities, labs, or research that shows commitment to staying current.

The skills gap won’t be closed through traditional hiring alone. Consider career changers who bring different perspectives and invest in internal upskilling rather than only external hiring. According to the Fortinet report, 52% of leaders say the issue isn’t headcount, it’s having people with the right skills.

Training programs alone don’t create lasting capability. Teams need an environment that supports continuous learning and experimentation. AI readiness is a moving target, and culture determines whether teams keep pace.

Dedicate time for skill development, not just incident response. Without dedicated time, training becomes aspirational rather than actual. Consider structured learning programs with accountability that ensure development happens consistently.

Teams need space to experiment with AI tools and techniques in lab environments without production risk. Encourage exploration of how attackers might use generative AI against your organization. Practical environments like Proving Grounds enable testing and skill development without consequences.

Mentorship programs that pair AI-skilled staff with those developing capabilities create multiplicative effects. Regular team sessions to share learnings from training, incidents, and experiments build collective intelligence. Cross-functional collaboration between offensive and defensive teams, red team and blue team, ensures insights flow in both directions.

Reinforce that AI tools enhance skilled professionals rather than replace them. Celebrate practical problem-solving, not just tool proficiency. Make clear that foundational skills remain career-essential regardless of how AI capabilities evolve.

Keep your program aligned with evolving threats. Regularly evaluate team capabilities, adjust training investments based on assessment results, and track both AI skill development and foundational competency maintenance.

Building an AI-ready team happens in phases.

Assess current team capabilities across both foundational and AI-specific skills. Identify critical gaps based on organizational AI exposure and risk. Ensure foundational training is in place, PEN-200/OSCP for penetration testers and equivalent training for other roles. Establish baseline metrics for tracking progress.

Implement role-specific AI training programs and prioritize hands-on training with realistic scenarios. Begin offensive AI training to strengthen defensive capabilities through LLM Red Teaming. Create lab environments for safe experimentation.

Integrate AI capabilities into daily operations. Establish continuous learning programs through Learn One subscriptions. Implement regular skill assessments and adjust training accordingly. Build knowledge-sharing practices into team culture.

Monitor skills gap reduction over time, time to proficiency for new AI tools, incident response effectiveness before and after AI integration, team confidence in AI-related capabilities, and retention rates, because professionals want development opportunities.

Building an AI-ready team requires both AI-specific skills and strong foundational competencies. AI tools amplify the capabilities of skilled professionals; they don’t replace the need for those skills. Hands-on training builds capabilities that theoretical learning cannot. Understanding offensive AI techniques makes defensive teams more effective. Continuous learning is essential because AI capabilities evolve rapidly.

The organizations that build truly AI-ready teams will have a significant advantage, not because they adopted AI tools first, but because they developed teams capable of using those tools effectively while maintaining the human judgment and problem-solving capabilities that AI cannot replicate. By implementing proper security measures and investing in workforce development, you can defend against increasingly sophisticated cyber attacks. Start with a foundation of hands-on skills, layer AI capabilities strategically, and create a culture of continuous learning.Build an AI-ready team through hands-on training that develops real-world capabilities. Start with foundational skills through PEN-200, develop offensive AI expertise through the LLM Red Teaming path, or enable continuous team development with Learn One.