Detection engineering follows the same pattern. A rule that produced useful signals 18 months ago may generate mostly benign activity today. New attacker techniques may also create gaps that did not exist when the rule was written. Continuously evaluating detection fidelity and coverage can make improvement part of day-to-day operations without requiring a dedicated detection engineer for every environment.
Threat intelligence has an even shorter useful life. Teams may subscribe to high-quality sources and still struggle to translate new reports into hunts before the information loses relevance. AI can help interpret new intelligence, identify the techniques that matter to a specific environment and execute the corresponding hunts quickly.
Alert investigation may be the clearest example. A human analyst typically must gather endpoint, identity, network, email or cloud context, correlate the evidence, reach a conclusion and document the result. For a small team, repeating that process across dozens or hundreds of alerts is unsustainable. AI can perform much of the evidence gathering and correlation, giving the analyst a completed investigation or a clearly documented escalation point.
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