Autonomous soc—neuro-cognitive security orchestration and response system
View Patent ↗The present invention discloses a neuro-cognitive cybersecurity orchestration and response system that enables autonomous, real-time decision-making within a security operations center (SOC). The system integrates telemetry agents, drift analysis engines, trust computation logic, reinforcement learning modules, and dynamic enforcement interfaces to form an adaptive control architecture. Unlike conventional rule-based SOAR systems, this invention performs real-time mitigation based on evolving cognitive and behavioral telemetry. The architecture supports closed-loop feedback and reinforcement-driven policy recalibration, enabling the SOC system to respond to threats such as phishing or lateral movement without human intervention. The design includes modular, hardware-executable components and enables deployment in hybrid and agentless environments, ensuring rapid and robust cyber defense. This technical solution to a technical problem is patent-eligible under USPTO guidelines.
1 . An autonomous neuro-cognitive security orchestration and response system, comprising: one or more processors and a memory storing instructions that, when executed by the one or more processors, cause the system to perform operations including:
receiving, via a telemetry collection interface, behavioral telemetry data associated with one or more user sessions, the behavioral telemetry data being obtained from at least one of endpoint-based telemetry agents or cognitive profiling systems;
analyzing the received behavioral telemetry data to compute a cognitive drift score representing a deviation from a historical neuro-cognitive baseline associated with a monitored user or entity;
computing, based on the cognitive drift score and contextual session information, a session-specific trust score indicative of a current trust posture of the monitored user or entity;
determining, using a reinforcement learning-based decision process, an adaptive enforcement policy by evaluating the cognitive drift score, the session-specific trust score, and historical outcome feedback associated with prior enforcement actions;
generating, based on the adaptive enforcement policy, a policy adjustment output specifying one or more security enforcement actions selected from access restriction, session suspension, adaptive authentication, or resource isolation;
executing, via an enforcement interface coupled to one or more enterprise IT assets, the one or more security enforcement actions specified by the policy adjustment output; and
updating, using outcome data resulting from execution of the one or more security enforcement actions, at least one of: (i) parameters of the reinforcement learning-based decision process, or (ii) trust threshold values associated with the session-specific trust score, wherein the system operates in a closed-loop feedback architecture that continuously refines enforcement policy selection based on observed behavioral outcomes without requiring human analyst intervention.
2 . The system of claim 1 , wherein the behavioral telemetry data comprises at least one of typing cadence, cursor movement dynamics, application interaction timing, or response latency.
3 . The system of claim 1 , wherein the cognitive drift score is computed by comparing real-time behavioral telemetry data to a stored historical neuro-cognitive baseline associated with the monitored user or entity.
4 . The system of claim 1 , wherein the session-specific trust score is computed using a weighted combination of the cognitive drift score and contextual session parameters including time-of-day, access context, or resource sensitivity.
5 . The system of claim 1 , wherein the reinforcement learning-based decision process assigns reward values to enforcement outcomes based on observed effectiveness of prior security enforcement actions.
6 . The system of claim 1 , wherein the policy adjustment output specifies a graduated enforcement action selected based on a severity level associated with the session-specific trust score.
7 . The system of claim 1 , wherein the enforcement interface is configured to interact with at least one of identity and access management systems, network access control systems, or application-level access control components.
8 . The system of claim 1 , wherein the closed-loop feedback architecture updates the reinforcement learning-based decision process using outcome data indicative of success or failure of executed enforcement actions.
9 . The system of claim 1 , wherein the system autonomously performs the operations without requiring manual review or approval by a human security analyst.
10 . A method for autonomous neuro-cognitive security orchestration and response, comprising: receiving, by one or more processors, behavioral telemetry data associated with an active user session, the behavioral telemetry data being obtained from at least one of an endpoint-based telemetry agent or a cognitive profiling system;
computing, based on the behavioral telemetry data, a cognitive drift score representing a deviation of the active user session from a historical neuro-cognitive baseline associated with a monitored user or entity;
computing, based on the cognitive drift score and contextual session information, a session-specific trust score indicative of a current trust posture of the monitored user or entity; selecting, using a reinforcement learning-based decision process, an adaptive enforcement policy by evaluating: (i) the cognitive drift score, (ii) the session-specific trust score, and (iii) historical outcome feedback derived from prior security enforcement actions;
generating, based on the selected adaptive enforcement policy, a policy adjustment output specifying one or more security enforcement actions; automatically executing, in real time and without human analyst intervention, at least one of the one or more security enforcement actions on one or more enterprise IT assets associated with the active user session; and
updating, using outcome data resulting from execution of the at least one security enforcement action, at least one of: (a) parameters of the reinforcement learning-based decision process, or (b) trust threshold values used to compute the session-specific trust score, wherein the method is performed as a closed-loop feedback process that autonomously refines future enforcement policy selection based on observed behavioral outcomes.
11 . The method of claim 10 , wherein the behavioral telemetry data comprises at least one of typing cadence, cursor micro-movements, application focus transitions, or interaction response timing.
12 . The method of claim 10 , wherein computing the cognitive drift score comprises comparing real-time behavioral telemetry data to a historical neuro-cognitive baseline associated with the monitored user or entity.
13 . The method of claim 10 , wherein computing the session-specific trust score comprises weighting the cognitive drift score based on contextual session parameters including access location or resource classification.
14 . The method of claim 10 , wherein the reinforcement learning-based decision process evaluates enforcement outcomes using reward signals derived from observed behavioral normalization following execution of enforcement actions.
15 . The method of claim 10 , wherein executing the at least one security enforcement action comprises modifying access privileges associated with the active user session.
16 . The method of claim 10 , wherein executing the at least one security enforcement action comprises initiating adaptive authentication for the active user session.
17 . The method of claim 10 , wherein updating the parameters of the reinforcement learning-based decision process comprises adjusting policy selection preferences based on historical enforcement effectiveness.
18 . The method of claim 10 , wherein updating the trust threshold values comprises recalibrating acceptable deviation ranges associated with the session-specific trust score.
19 . The method of claim 10 , wherein the closed-loop feedback process operates continuously during the active user session.
20 . The method of claim 10 , wherein the method is performed without generating enforcement recommendations requiring human approval.
21 . The method of claim 10 , wherein the behavioral telemetry data is received from at least one of an endpoint-based telemetry agent or an agentless telemetry source.
22 . The method of claim 10 , wherein the reinforcement learning-based decision process is initialized using historical enforcement outcome data.
23 . The method of claim 10 , wherein the policy adjustment output specifies enforcement actions with varying degrees of restrictiveness based on the session-specific trust score.
24 . The method of claim 10 , wherein the enforcement actions are executed in real time on enterprise IT assets associated with the active user session.
25 . The method of claim 10 , wherein the closed-loop feedback process refines future enforcement policy selection using outcome data aggregated across multiple user sessions.
26 . The method of claim 10 , wherein the method autonomously adapts enforcement behavior over time without reliance on predefined static response playbooks.