Autonomous Threat Mitigation Agents for Adaptive Security Response
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Solution Overview
Problem
The complexity of computer attacks is increasing, and existing threat mitigation systems struggle to effectively detect and respond to sophisticated and evolving threats across multiple computing systems and subsystems.
Innovation Solution
An autonomous agent-based system that autonomously defines, executes, and evaluates investigation/remediation plans using AI/ML to address security events, providing real-time threat mitigation across multiple computing platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional threat mitigation systems are used to monitor security events, then basic detection capability is maintained, but the system cannot effectively detect and respond to sophisticated and evolving threats due to increasing attack complexity
Solution Approach 1:
The system employs dynamic AI/ML models that continuously learn and adapt to new threat patterns. The autonomous agents update their detection algorithms in real-time based on emerging threat intelligence, enabling the system to maintain effectiveness against evolving sophisticated attacks rather than relying on static signature-based detection.
Solution Approach 2:
The autonomous agents perform self-directed investigation and remediation without human intervention. They autonomously analyze security events, determine appropriate responses, execute remediation actions, and learn from outcomes to improve future detection and response capabilities, enabling the system to serve itself in countering sophisticated threats.
2Productivity
If manual investigation and remediation processes are used, then thorough analysis is possible, but response time is too slow to address security events effectively
Solution Approach 1:
The system replaces manual mechanical investigation processes with autonomous AI-powered agents that can analyze security events, investigate threats, and execute remediation at machine speed. These agents process security data, correlate events, and implement responses automatically, achieving both high response speed and thorough investigation through advanced algorithms rather than human operators.
Solution Approach 2:
Autonomous agents act as intermediaries between security event detection and human analysts. They perform initial investigation, triage, and remediation actions, freeing human analysts to focus on complex cases requiring expert judgment. This intermediary layer accelerates response time while maintaining investigation quality through automated analysis capabilities.
3Reliability
If comprehensive security monitoring across multiple computing systems is implemented, then threat detection coverage is improved, but system complexity increases making it difficult to manage and respond to events
Solution Approach 1:
The system segments security monitoring across multiple autonomous agents, each responsible for specific computing systems or threat types. These modular agents independently monitor their assigned domains, reducing the complexity burden on central management while maintaining comprehensive coverage. Each agent operates semi-independently, simplifying the overall system architecture despite monitoring multiple systems.
Solution Approach 2:
The autonomous agents are designed with multi-functionality, capable of performing detection, investigation, analysis, and remediation across diverse computing systems and threat types. This universal design reduces management complexity by using the same agent architecture for different platforms and threats rather than requiring specialized systems for each scenario.
4Productivity
If automated response actions are executed without human review, then response speed is maximized, but risk of incorrect remediation increases
Solution Approach 1:
The system implements feedback loops where autonomous agents monitor the outcomes of remediation actions they execute. When incorrect or ineffective remediation occurs, the agents learn from the feedback and adjust their decision-making algorithms. This continuous learning from real-world outcomes enables the system to improve remediation accuracy over time while maintaining automated response speed.
Solution Approach 2:
Before executing remediation actions, the autonomous agents perform preliminary analysis and validation to assess the appropriate response. They evaluate security events against known threat patterns, determine confidence levels, and select remediation actions based on pre-trained knowledge. This preliminary assessment reduces the risk of incorrect remediation while maintaining fast automated response for high-confidence cases.
Data Source
AI summary
A computer-implemented method, computer program product and computing system for receiving an alert concerning an event within a computer platform; autonomously defining an investigation/remediation plan for addressing the event within the computer platform based upon one or more available resources; autonomously executing the investigation/remediation plan to address the event within the computer platform; autonomously determining an efficacy level for the investigation/remediation plan; and autonomously effectuating a remedial action based, at least in part upon the determined efficacy level.