AI in Security Operations Centers Fails Without Good Data, Vendor Says
Security operations centers deploying AI are often failing not because the technology is inadequate, but because they lack sufficient context about their environment, according to Daylight, a security vendor.
The company said that without integrating data on identities, assets, policies, and behavioral history, AI simply processes incomplete information faster. The result is faster but not better security investigations.
Successful AI-driven SOC operations require building rich contextual data specific to each organization, Daylight said. Organizations must encode how their environment behaves and continuously refine decision-making logic to make AI effective.
Build Internally or Buy Externally
Companies face a choice: develop this capability in-house or work with a vendor that has already embedded contextual data into its operations. The decision hinges on available resources and existing security infrastructure.
Daylight is collaborating with cybersecurity analyst Oliver Rochford, formerly at Gartner, to create a buyer guide on implementing AI in SOCs. The guide targets security and IT decision-makers evaluating AI-enabled solutions.
What This Means for Operations Teams
For operations professionals, the takeaway is straightforward: AI tools are only as good as the data feeding them. Before adopting an AI-driven SOC solution, operations teams should assess whether their organization has the data infrastructure and context required to make AI effective.
This includes auditing existing data on user identities, asset inventories, security policies, and historical threat patterns. Gaps in any of these areas will limit AI performance regardless of the vendor's capabilities.
Learn more about AI for Operations or explore the AI Learning Path for Cybersecurity Analysts to understand how AI fits into your security operations workflow.
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