Tradition security measures, while offering a baseline level of protection, greatly rely on specified names and a narrow definition of the “abnormal”. They usually follow a responsive approach, may be siloed, limiting information sharing, and lack the scalability to control the terabytes of data generated by today’s difficult IT systems.
Synthetic knowledge enters this area.
In this TechRepublic Premium have, written by Avya Chaudhary, discover more about this important security problem, get expert opinions and learn how to protect your business. The access includes guidance from Mary Ann Miller, VP missionary &, fraud executive assistant at Prove Identity, and Amit Patel, senior vice president at Consulting Solutions.
Featured word from the get:
A gold rush for relational AI is being revealed in a study by Bain, and it all starts at the very beginning of the struggle, or threat identification. Compared to the other five stages of the SANS event response type — planning, isolation, destruction, recovery and lessons learned – identification represents the biggest one area of AI adoption.
But, AI’s real strength lies in its ability to go beyond merely identifying threats to conducting strategic threat hunting. AI tools automatically create threat detection queries, search through the information, and identify possible threats without having to manually insert SQL queries.
AI these can also serve as a platform for developing hypotheses, assisting security analysts in developing their own instincts and guesswork into a planned threat prediction. Now that an analyst is able to use AI to look up inbound and outbound links and filter for specific procedures related to lateral motion without having to create specific questions for Server Message Block or attempts to establish remote desktop connections, can then identify lateral movement in event logs and community traffic logs.
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