Threat Detection AI monitors network, endpoint, identity, and cloud telemetry simultaneously using ML-based anomaly detection. Unlike signature-based tools that only catch known threats, behavioral anomaly detection identifies novel attack patterns — zero-days and living-off-the-land techniques that evade traditional detection. Every alert is mapped to MITRE ATT&CK framework — giving SOC analysts context on what technique is being used, what stage of the kill chain the attacker is at, and what the likely next steps are. Sub-second detection latency from raw telemetry to SOC alert. All alerts enriched with asset criticality, user risk score, and historical context before reaching an analyst.

Automated Response executes containment actions for confirmed, low-risk threats within pre-approved playbooks — no waiting for an analyst to be available at 03:00. Autonomous actions are limited to reversible, low-impact steps: blocking a known malicious IP, quarantining a suspected endpoint from the network, revoking a compromised session token. All actions are logged with full justification. High-impact actions — network segmentation changes, account lockouts, data access revocation — require SOC analyst approval before execution. The analyst is presented with a recommended action, the evidence supporting it, and a one-click approval or override. Mean Time to Respond: 3 minutes vs 4 hours manual. Every automated action can be rolled back.

80% of breaches involve compromised credentials. Identity Analytics monitors every user's access pattern and flags deviations that indicate credential compromise or insider threat. Impossible travel detection: user logs in from London at 09:00 and New York at 09:30 — physically impossible, immediately flagged. Unusual access: finance director accessing engineering code repository at 02:00 — pattern anomaly. Lateral movement: service account accessing systems it has never accessed before — attacker moving through the network. All identity flags are presented to the SOC as investigation priorities — no accounts are locked automatically without SOC analyst decision.

Cloud Security Posture Management monitors AWS, Azure, and GCP environments continuously for misconfigurations, over-privileged IAM, exposed storage, and cloud-native attack patterns. Common findings: S3 buckets with public read access, IAM roles with administrator privileges attached to EC2 instances, security groups allowing 0.0.0.0/0 ingress on sensitive ports. Each finding is scored by risk level and mapped to CIS Benchmarks and cloud-native security frameworks. Remediation recommendations include the exact infrastructure-as-code change needed. All remediation requires engineer approval — CyberSecOS provides the finding, the risk context, and the fix; cloud engineers execute.