AI in Cybersecurity

Cybersecurity AI uses machine learning for detection, prioritization, and response across email, endpoint, network, identity, and cloud—while a growing subset addresses security of AI applications (prompt injection, model abuse, and supply-chain risks).

Attack volume and alert fatigue make ranking and detection models operationally valuable. Separating “AI for security” from “security for AI” clarifies buying categories.

• Adversarial evasion and concept drift
• False positive cost for SOC teams
• Integration across security stacks
• Emerging generative attack techniques

ML-based detection is mainstream in email and endpoint categories. AI application security and LLM firewalls are newer product lines still maturing in enterprise standards.

Is every security vendor an AI company?

No. Brel tags Cybersecurity when AI/ML is a primary disclosed capability, not merely rule engines marketed as AI.

• CISA — cybersecurity guidance portal
• NIST AI RMF