Cyber Security in the Age of AI

Why the Threat Landscape Has Changed and What Your Development and Security Teams Need to Know Now

The cybersecurity skills gap has widened, not narrowed. In 2026, the global cybersecurity workforce gap has reached more than 4.8 million unfilled positions. More concerning than the headcount shortage is what the SANS/GIAC 2026 Cybersecurity Workforce Research Report — the most authoritative annual study of the profession — found when it surveyed 947 security leaders: for the first time, 60% of CISOs now cite skills gaps rather than headcount shortfalls as their primary concern.

The problem is not that organisations cannot hire enough security people. It is that the people they have do not have the right skills for the threats they face. AI has made that mismatch critical.

AI-powered attacks surged 89% year-over-year between 2025 and 2026. Attackers are using machine learning to mutate malicious code in real time, automate vulnerability discovery, and personalise phishing at scale. The response-time window between attack and breach — already compressed by modern tooling — has been cut further. Traditional incident response playbooks designed for methodical, step-by-step investigation are increasingly mismatched to this reality.

At the same time, 49% of cybersecurity leaders are concerned that AI will increase both the volume and sophistication of attacks. The World Economic Forum has identified cross-functional AI fluency as one of the most urgent global workforce priorities — not just for dedicated security professionals but for every developer writing code that will be exposed to AI-assisted adversaries.

 

The most consistent finding across the 2026 security research is stark: most security incidents trace to application or supply-chain issues, but most security professionals come from networking or operations backgrounds. Engineers who can write secure code and reason about threats — who understand both how systems are built and how they can be attacked — are scarce and exceptionally well compensated.

Three skill areas offer the highest leverage for development teams looking to close this gap:

• Threat modelling — teaching engineers to think systematically about how systems can fail and how attackers exploit those failures. Fewer than 40% of organisations currently do this, despite it being among the highest-return security investments.
• Secure coding practices — reducing the introduction of OWASP Top 10 vulnerabilities at source. Academic and OWASP studies find 30–40% of AI-generated code contains at least one security vulnerability, making secure code review a more urgent skill than ever.
• AI security — understanding prompt injection, model poisoning, supply-chain risks for AI systems, and how to secure agentic deployments that have real authority to query databases, modify code, and trigger workflows.

For teams in regulated industries, 2026 brings new governance obligations. The EU AI Act's requirements for high-risk AI systems — including transparency, human oversight, and audit logging — are now shaping how security and compliance teams approach AI deployments. The intersection of cybersecurity, data privacy (GDPR, PCI DSS), and AI governance is the most complex and under-resourced skills domain in enterprise technology today.

 

→ Threat Modelling for Developers  —  Practical threat modelling techniques for software developers — how to systematically identify attack surfaces, model adversary behaviour, and prioritise security investment.

→ OWASP Top 10 Practical Web Application Security  —  Hands-on course covering the OWASP Top 10 vulnerabilities with practical exercises in identifying and fixing each class of issue in real application code.

→ Secure Web Application Development  —  Security-first development covering authentication, authorisation, input validation, and common attack patterns from a developer perspective.

→ AI Ethics, Governance and the EU AI Act  —  Practical, no-code course for compliance, legal, and risk teams on the EU AI Act requirements and building governance frameworks for AI deployments.

→ AI Security: Attacks and Defences  —  Identify and mitigate prompt injection, data leakage, and model poisoning through live exercises. For developers and security engineers deploying AI systems in production.

→ CISSP  —  Comprehensive preparation for the CISSP certification covering all eight security domains. For security professionals targeting leadership-level roles.

→ GDPR for IT and Software Professionals  —  GDPR requirements from a developer and architect perspective — privacy by design, data handling obligations, and technical controls.

 

Build Your Team's Skills with JBI Training

JBI Training delivers instructor-led cybersecurity, secure coding, threat modelling, AI security, and GDPR courses for corporate teams. Public courses in London and live virtual delivery.

www.jbinternational.co.uk/courses/security