Uses AI and machine learning to identify threats instantly across networks, applications, and endpoints.
Continuous monitoring ensures faster detection of anomalies, malware, phishing attempts, and unauthorized access.
Integrates with EDR, IDS, and threat intelligence feeds to flag risks as they arise.
Autonomous Incident Response
Implements automated workflows that instantly respond to detected threats — such as isolating infected devices, blocking malicious IPs, or revoking compromised credentials.
Enables faster containment and limits the spread of cyberattacks without waiting for manual intervention.
Reduces MTTD (Mean Time to Detect) and MTTR (Mean Time to Respond).
Risk Mitigation
Proactively evaluates risks and suggests preventive actions before attacks can cause damage.
Uses AI models to assess vulnerability impact, exposure level, and likelihood of exploitation.
Supports compliance by identifying policy gaps and offering guidance for improvement.
LLM-Powered Intelligence (Large Language Model Integration)
Uses AI language models (like GPT) to
- Analyze massive volumes of threat data.
- Detect patterns from dark web chatter, phishing campaigns, and zero-day exploits.
- Provide context-aware threat insights and prescriptive recommendations.
Supports automated adversarial testing, compliance automation, and real-time detection for AI-enabled enterprises.
