Establish baselines and detect suspicious behavior - RLHF
AI that learns based on feedback
Data Governance models access rights mostly based on a person’s job description, their position in an organizational structure or on a certain team. This applies intra and inter company. However, what happens if a legal identity gets assumed by a bad actor to gain access and do damage or if an insider goes rogue? This is where sophisticated, AI based detection technology comes in. User behavior gets continuously baselined and monitored for deviations that could signal imminent danger.
Reinforced Learning with Human Feedback(RLHF) for Attack Detection and Behavior Anomaly Verdicts:
AI Engine utilizes reinforced learning algorithms, which allow the AI engine to learn from interactions with its environment and make informed decisions.
In the context of Theom, this approach enables the engine to detect attacks by continuously analyzing data access patterns, system interactions, and user behavior.
AI Engine incorporates human feedback into the learning process, benefiting from the expertise of security professionals who provide guidance and validation for attack detection.
Combining reinforced learning with human feedback makes Theom's AI engine adept at recognizing attack patterns, identifying behavior anomalies, and providing accurate verdicts.