DESC:The proposed system incorporates a multi-layered architecture consisting of telemetry ingestion (100), pre-processing (101), feature extraction (102), agentic reasoning engine (103), dynamic risk scoring (104), threat intelligence fusion (105), and autonomous threat response (106).

The system processes large volumes of real-time telemetry, applies dynamic scoring algorithms, and autonomously responds to detected threats. Feature extraction mechanisms derive contextual and behavioral features from telemetry, enhancing prediction accuracy.

The agentic reasoning engine continuously optimizes decision-making by formulating goals, setting dynamic priorities, and adapting risk policies based on feedback.

Risk scoring models combine supervised, unsupervised, and reinforcement learning approaches to dynamically assess asset, user, and network risks.

Threat intelligence fusion layer ingests, normalizes, and correlates multiple threat feeds, aligning internal behaviors with external threat signals.

Autonomous response orchestrator initiates multi-tiered threat responses including isolation, containment, and recovery actions.
,CLAIMS:1. An agentic artificial intelligence system for cybersecurity comprising a telemetry ingestion engine, an agentic reasoning engine, a risk scoring module, a threat intelligence fusion layer, and an autonomous response orchestrator.
2. The system of claim 1, wherein the agentic reasoning engine dynamically adapts its decision logic based on feedback loops from threat response outcomes.
3. The system of claim 1, wherein the autonomous response orchestrator initiates remediation actions selected from the group consisting of endpoint isolation, traffic blocking, credential revocation, forced logout, and incident escalation.
4. The system of claim 1, wherein the telemetry ingestion engine collects data including but not limited to endpoint logs, network flows, authentication events, email metadata, and cloud service activity logs.
5. The system of claim 1, wherein the risk scoring module employs supervised machine learning models to classify user and device behavior into predefined risk categories.
6. The system of claim 1, wherein the risk scoring module further applies unsupervised anomaly detection to identify previously unknown or novel threats.
7. The system of claim 1, wherein the agentic reasoning engine autonomously recalibrates risk scoring thresholds based on organizational security posture changes.
8. The system of claim 1, wherein the threat intelligence fusion layer standardizes and normalizes threat indicators received from heterogeneous sources including MITRE ATT&CK mappings, STIX/TAXII feeds, and vendor-specific threat data.
9. The system of claim 1, wherein the autonomous response orchestrator dynamically selects a response based on an optimization function balancing risk severity, asset criticality, and operational impact.
10. The system of claim 1, wherein the system supports integration with external security orchestration, automation, and response (SOAR) platforms to execute complex playbooks.
11. The system of claim 1, wherein the agentic reasoning engine utilizes reinforcement learning techniques to optimize future threat response strategies.
12. The system of claim 1, wherein the risk scoring module incorporates contextual metadata including user identity, asset classification, geolocation, and recent activity history into risk computation.
13. The system of claim 1, wherein the agentic reasoning engine establishes an internal goal hierarchy comprising urgent containment actions, medium-term investigation tasks, and long-term resilience objectives.
14. The system of claim 1, wherein the autonomous response orchestrator triggers multi-step containment workflows based on evolving threat conditions.
15. The system of claim 1, wherein the system provides explainability outputs indicating why a particular risk score or remediation action was assigned, enabling auditability and compliance reporting.
16. The system of claim 1, wherein the telemetry ingestion engine detects data exfiltration attempts by correlating upload behavior anomalies with known data loss patterns.
17. The system of claim 1, wherein the agentic reasoning engine supports adversarial behavior simulation to preemptively test organizational defenses.
18. The system of claim 1, wherein the system continuously monitors policy deviations and automatically updates adaptive security controls accordingly.
19. The system of claim 1, wherein risk scores are recalculated in real-time based on the cumulative impact of sequential events rather than isolated event evaluation.
20. The system of claim 1, wherein the agentic reasoning engine autonomously escalates critical threat incidents to human analysts only when uncertainty exceeds a predefined confidence threshold.