Vectra for Critical National Infrastructure (CNI)

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How to secure CNI using network detection and response

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Identify Attack Scenarios Common in CNI

  • Visibility into CNI attacks in cloud/SaaS and data center workloads and devices in OT, industrial IoT, and ICS networks
  • Expose attacker command-and-control communications hidden in encrypted tunnels – without using decryption
  • Detect early reconnaissance behaviors to stop attackers from accessing and exploiting critical management systems
  • Identify lateral movement to prevent the theft of administrative and account credentials used to escalate a CNI attack

“Vectra provides automated detections with context to make fast, informed decisions.”

Dave Buffo

Senior Security Administrator

Tri-State Generation and Transmission Association, a provider of wholesale electric power throughout Colorado, Nebraska, New Mexico and Wyoming

Driven by AI, Vectra automatically detects, prioritizes and responds to in-progress cybersecurity attacks against CNI in energy, utilities, healthcare and finance. Vectra condenses weeks of manual CNI security investigations into minutes, enabling analysts to take immediate action to prevent damage and data theft.

Automate threat detections

AI-derived machine learning models automate the detection of attackers, suppress noise from benign alerts, and prioritize the highest-risk threats for faster response.

Empower threat hunters

Launch deeper investigations into CNI security incidents detected by Vectra as well as other security stack controls, and hunt for hidden cyberthreats with greater efficiency.

Achieve greater visibility

Gain high-fidelity visibility into all network traffic, including the actions of cloud and data center workloads, users and devices – leaving attackers with nowhere to hide.

Capture and enrich metadata

Collects metadata from all network traffic and workloads, enriching it with security insights and context at a speed and scale that is impossible for humans to achieve.

Government Cybersecurity

Fastest attack detection. Fastest incident response. Fastest threat hunting.

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AI-driven Network Detection and Response (NDR)

Attacker behavior detection

Self-learning threat behavior models from data science and security research automatically identify malicious activity, fortify key security attributes and security patterns, normal patterns, precursors, account scores, host scores, and correlated attack campaigns.

Real-time threat hunting

Metadata extracted from all network traffic is enriched with security insights so you know where and what to hunt. SOC teams increase productivity and reduce attacker dwell time by integrating Vectra with your current security ecosystem for end-to-end response automation.

AI and machine learning

AI and ML scale-up to analyze and prioritize huge volumes of threat events to give SOCs the right information at the right time. High-fidelity alerts about in-progress attacks enable faster, informed responses and quick, decisive enforcement actions for a superb ROI.

CDM DEFEND: See What’s Happening on the Network

Aligned with Continuous Diagnostics and Mitigation (CDM) Dynamic and Evolving Federal Enterprise Network Defense (DEFEND) Phase 3, Vectra shows what’s happening in federal and government agencies' cloud, data center, and IoT networks to harden security posture.

Vectra addresses critical elements of Phase 3:

  • Protect: Automatically detect,  triage and prioritize threats that evade boundary protection, enabling faster  mitigation of high risk attacks.
  • Manage: Integrate with your existing security ecosystem – from endpoint detection and response to  orchestration and security information event management – for end-to-end threat management.
  • Respond: Automatically share  critical threat behavior data – including context about attack campaigns and  forensic evidence.
  • Improve: Advanced machine learning algorithms derived from AI continuously learn, becoming more intelligent and operationally effective over time.

How to Detect, Prioritize, and Respond to Federal and Government Cyberattacks

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