“Imagine an AI agent that autonomously collects indicators of compromise [IOCs] from multiple threat feeds, correlates them with internal telemetry, enriches the data with context from OSINT and CTI [cyber threat intelligence] repositories, and then drafts a structured alert for an analyst.” Instead of waiting for a SOC team to pivot manually across different platforms, the agent executes the pivoting automatically, flags anomalies, and prepares a recommended response playbook.
Geenens believes his suggested approach, like many agentic AI use cases presented here, addresses two major cybersecurity pain points: scale and speed. “Analysts are drowning in alerts and lack the time to connect dots across multiple sources,” he says. Agentic AI can effectively supplant repetitive, high-volume correlation tasks. More important, it closes the gap between detection and mitigation, enabling analysts to focus on validation and strategy rather than operations. “In practice, this doesn’t replace humans, but amplifies expertise while cutting through noise.”
4. Augmenting security talent
Another big problem in cybersecurity doesn’t involved technology — it’s the current talent gap, and AI agents provide that most practical answer, says Rahul Ramachandran, generative AI product management director at Palo Alto Networks.
