Cybersecurity in the AI context covers two intertwined disciplines: securing AI systems themselves (against prompt injection, model theft, training-data poisoni
Cybersecurity in the AI era runs both directions. Defending with AI: models triage alerts, hunt anomalies, summarize incidents, and automate SOC response. Defending AI itself: securing models, training data, prompts, and agent tool-chains against injection, poisoning, theft, and abuse, a new attack surface layered on classic security engineering.
AI raised stakes on both sides: attackers wield it for convincing phishing, deepfakes, and faster exploitation, while every deployed agent adds attackable capability inside the perimeter. Security programs that treat AI systems as crown-jewel assets, inventoried, tested, monitored, are the 2026 baseline, not the vanguard.
Scaled, personalized phishing; voice and video deepfakes for fraud; faster vulnerability discovery; and automated attack chains. Volume and believability rise together: pushing defenses toward AI-assisted detection and stronger out-of-band verification.
New asset classes (models, prompts, training data, vector stores) and new attack classes (injection, poisoning, model extraction, agent tool abuse) on top of everything classic. Frameworks like the OWASP LLM Top 10 catalog the delta.
Inventory AI systems and their permissions, threat-model the agentic ones (what could an injected agent do?), red-team the highest-risk paths, and wire AI telemetry into existing security monitoring. Govern AI assets like any critical system.