Intrusion Detection and Prevention Systems (IDPS) analyze traffic for known threats, but attackers move faster. Learn why IDPS is not enough and how AI-driven threat detection uncovers real threats in real time.
IDPS solutions monitor network traffic for known attack patterns, but modern attackers use evasive techniques that bypass signature-based detection. Once inside, they move stealthily across networks, cloud workloads, and identity systems—areas where IDPS has no visibility. Attackers exploit these blind spots to escalate privileges, move laterally, and exfiltrate data undetected.
Attackers modify malware, use polymorphic techniques, or leverage encrypted traffic to avoid detection.
IDPS trusts authenticated users, failing to detect stolen credentials or privilege escalation.
IDPS focuses on network perimeters but lacks visibility into cloud, SaaS, and identity-based threats.
In a Scattered Spider–style attack (as illustrated below), an Intrusion Detection and Prevention System (IDPS) is largely ineffective—not because it’s broken, but because it’s built to stop known attack signatures, not detect modern adversaries who live off the land, abuse identity, and operate inside encrypted and trusted paths.
IDPS is designed to detect known attack patterns, but it fails against sophisticated attackers who use novel, fileless, and credential-based techniques. Security teams need an approach that goes beyond signatures to detect attacker behavior in real time.
IDPS relies on predefined signatures and traffic analysis, but:
IDPS detects known threats, but it can’t stop attackers who operate without malware or known signatures. The Vectra AI Platform provides real-time threat detection across network, cloud, and identity layers, closing security gaps that IDPS can’t.
With Vectra AI, you can detect threats that IDPS overlooks—before they escalate into breaches.
IDPS focuses on known threats, while Vectra AI detects active attacks beyond signature-based defenses. Here’s how they compare: