Think of your company’s network as a city. AI gives both the police and the criminals super-powered binoculars and autopilot cars. Defenders use AI to spot unusual behavior and block attacks faster than humans can. Hackers use AI to scan for weak doors, write convincing scam messages, and automate break‑ins at scale.
For defenders, AI reduces the time and effort needed to detect, investigate, and respond to cyber threats that are too fast and too complex for human-only teams. It helps close skills gaps, automate routine monitoring, and react in near real time. For attackers, AI lowers the skill barrier and cost of launching sophisticated, targeted, high‑volume attacks.
Defensible advantage tends to come from proprietary telemetry (logs, network data, endpoint data), integration into existing SOC workflows, and continuously updated AI models trained on real attack data rather than generic public datasets.
Hybrid
Vector Search
High (Custom Models/Infra)
High-volume streaming telemetry (network, endpoint, identity logs) creates ingestion and real-time inference bottlenecks; LLM-based analysis also faces context window and latency/cost constraints for large enterprises.
Early Majority
Differentiation typically comes from depth and breadth of telemetry (endpoints, identity, cloud), quality and freshness of threat-intelligence training data, low false-positive rates in anomaly detection, and tight integration into incident response and SOC tooling rather than from generic AI models alone.