Cybersecurity AI for the modern network

Vectra AI applies ML to cybersecurity in a domain-specific, behavior-centric way. Unlike traditional anomaly detection systems that flag anything unusual, Vectra's ML threat detection models are purpose-built to identify attacker behaviors across network, cloud, and identity.

Machine learning is deeply integrated into Vectra AI’s detection engineering process, which aligns each model with real-world attacker techniques. These models are trained on high-fidelity behavioral data and tested against diverse attack scenarios. This ensures a high signal-to-noise ratio that minimizes false positives while surfacing truly malicious activity.

With more than 170 AI models and 36 AI patents, Vectra AI detects lateral movement, account compromise, and command-and-control communications using comprehensive strategies including attacker behavior modeling AI and behavioral analytics.

Our AI reduces SOC alert fatigue by replacing generic anomaly alerts with prioritized, correlated threat signals. Vectra AI’s system uses a triad of AI-powered tools — AI detection, AI agents (triage, stitching, and prioritization) — to whittle down hundreds of thousands of alerts to just a few high-fidelity signals.

• AI triage automatically investigates and resolves benign modern network activities.
• AI stitching correlates threat behaviors across domains (network, identity, cloud) to form cohesive attack narratives.
• AI prioritization AI ranks entities based on attack context and entity importance.

This approach drastically reduces analyst workload, improves mean time to detection (MTTD), and enables proactive incident response.

In modern cybersecurity, AI and ML are indispensable for keeping up with the speed, volume, and sophistication of modern attacks. Vectra AI advances this paradigm by delivering:

• AI-powered attack signal correlation to unify threat signals across hybrid cloud environments.
• AI in MITRE ATT&CK mapping to provide over 90% coverage of relevant TTPs.
• Zero-day threat prevention by detecting the intent behind actions, not just known signatures.

By modeling attacker behavior rather than chasing static indicators, Vectra’s ML-driven approach supports use cases like insider threat detection AI, lateral movement detection with AI, and behavioral fingerprinting — all crucial for operating in dynamic hybrid environments.

Vectra’s AI Detections are specifically engineered to focus on real threats — not just anomalies. They’re trained to detect behaviors consistent with attacker goals such as credential theft, lateral movement, privilege escalation, and data exfiltration.

• Domain-specific threat detection models ensure the detection logic is tailored for network, cloud, identity, and SaaS systems.
• Threat detection explainability is built-in, with context and prioritization to show why each alert matters.
• Detections are mapped to MITRE ATT&CK, helping security teams understand where in the kill chain the attacker resides.

The Vectra AI Platform is engineered specifically to support SIEM integration for AI threat detection, connecting with major SIEM platforms through robust APIs and pre-built connectors. Rather than flooding your existing systems with thousands of generic alerts, our platform transforms your SOC workflow automation by delivering investigation-ready incidents with complete attack narratives and behavioral context.

Our security platform deployment options adapt to any environment — whether you're running on-premises infrastructure, cloud-native architectures, or hybrid setups. The automated incident response workflow capabilities integrate directly into your existing processes, dramatically reducing analyst triage time while accelerating threat response across your entire security stack.

Vectra AI ingests telemetry from key cloud and identity platforms like AWS, Azure AD, Azure and Microsoft 365, and uses purpose-built AI models to transform this raw data into meaningful threat signals. These logs — whether authentication events, access tokens, or API calls — are continuously processed by AI engines that understand attacker behavior in cloud-native environments.

• Domain-specific AI models are trained on behavioral baselines within each cloud platform. This allows them to detect actions that deviate from normal user, role, or system behavior — such as suspicious logins, privilege escalations, or unusual access patterns.
• Our AI automatically correlates detections from cloud, identity, and network, forming a unified narrative of an attack as it unfolds across systems. This enables security teams to see the full scope of compromise (such as from an anomalous Azure AD login to a lateral movement in AWS).
• Once ingested, signals are enriched with context such as user privilege level, device identity, and organizational role. Our AI then applies prioritization logic based on risk factors, so only the most urgent threats rise to the top.

This approach ensures comprehensive coverage of multi-cloud environments, reduces alert volume, and allows defenders to see and stop malicious activity that spans across different cloud services and user identities.

Yes. The Vectra AI Platform provides comprehensive threat detection for the modern network. Our enterprise threat detection scalability processes millions of entities daily while maintaining precision.

Our multi-cloud visibility with AI correlates threat signals across your entire digital ecosystem, providing unified threat detection regardless of infrastructure complexity. In fact, this increased data volume actually enhances our AI's effectiveness — more entities mean richer behavioral baselines and more accurate detection. Our automated SOC alert triage processes massive data volumes while delivering the critical high-fidelity alerts that matter, typically reducing alert volume by more than 90% while improving detection accuracy.