For years, cybersecurity incident response was a bit like listening for smoke alarms in a mansion–if you heard a beep, you knew something was on fire. Signature-based detection did the job, but only after the damage started smouldering. Enter AI, which doesn’t just wait for smoke–it sniffs out strange cooking, checks the blueprints, and figures out if the fire is real, accidental, or part of an elaborate heist.
As threats get faster, sneakier, and more tailored, the response game is levelling up. AI is helping teams ditch the reactive whack-a-mole and step into a world of real-time context-aware defense. Think less panic button, more predictive detective with a knack for pattern recognition.
AI is particularly helpful with two key capacities, points out David Gruber, principal analyst at Enterprise Strategy Group (ESG). “First, the ability to more effectively apply threat intelligence in the detection, investigation, and response process,” he said. “This has long been a challenge for many security teams, and the recent application of AI is now threading helpful threat intel throughout the SecOps process. The second area is automation. AI is helping automate many of the more manual tasks previously associated with threat investigation, reducing the manual steps required to complete an investigation.”
Moving past signature-based detection
Learning and adapting from the emerging threats is an ability marketed with AI, promising a significant reduction in incident response time.
The ability comes in the form of machine learning models continuously analyzing network activity with smarter algorithms to identify anomalies leading to potential breaches, a stark improvement over the traditional, signature-based detection.
“AI-driven EDR systems are still new in the grand scheme of cybersecurity incident response, (particularly) impressive in the way they handle the massive amount of data that security operations teams have to sift through,” said Doug Kresten, CISO at Appfire. “Signature-based detection, and heuristics will still have their place, but they will be obfuscated under the AI covers.”
Gruber said AI cuts both ways, and that’s good news for security. “Recent methods are leveraging newer AI capabilities to expand the ability to detect the growing number of variants enabled by adversarial use of modern AI,” he said.
Leading the way in these use cases are vendors like CrowdStrike, Microsoft, and Palo Alto Networks. While CrowdStrike’s Falcon uses AI to analyze endpoint behavior in real-time to detect threats, Microsoft’s Defender combines machine learning and threat intelligence to identify and contain suspicious activities. Palo Alto, too, has been adding these capabilities to its Cortex XDR.
While AI gives detection a much-needed upgrade, apparent from offerings like Falcon and Defender, its contribution in incident analysis is almost equally gifting.
Querying up the catastrophe
Natural Language Processing (NLP)–the part of AI that teaches machines how to make sense of human language–has quietly slipped into the cybersecurity toolkit. It’s not flashy, but it’s surprisingly handy. In incident analysis, NLP can help sift through piles of logs, alerts, and reports to pull out the bits that actually matter.
“Natural language processing has allowed incident analysis to become much easier,” Kresten said.” “I believe it will have an impact on the type of employees that are hired and the skills that are expected of them. The majority of users today have no idea how the underlying architecture and machine-level coding work, like they needed to in the past to get anything done. They just know how to use the system.”
According to a recent study, “Natural Language Processing for Cybersecurity Incident Analysis”, NLP-driven chatbots reduced initial incident triage response times by 70% and achieved an 85% user satisfaction rate. Additionally, NLP-based summarization models like BERTSUM and T5 improved the comprehensibility of incident reports, reducing the time stakeholders needed to understand key points by 60%.
Popular Gen-AI-powered assistants like Splunk and QRadar are among the best available options for businesses to automate incident summarization, artifact analysis, or NLP troubleshooting.
On-the-fly response
While AI is great at detection and diagnosis, it’s increasingly stepping into the role of responder too. Security vendors like Palo Alto Networks are already integrating AI models that can isolate compromised systems, block suspicious IPs, and even patch vulnerabilities, often without waiting for human input.
However, within the context of response, both Gruber and Kresten agree on one thing: full autonomy is not here yet. “Most organizations still want a human in the loop to make the final decision,” noted Kresten. The reason? Trust–something that’s still being earned. As AI recommendations improve and prove themselves under fire, more businesses are expected to move toward full-scale automation, according to Gruber.
One of the key challenges with using AI in response is that some of the APIs required to automate response workflows fully are still under construction. This is expected to be accomplished over the coming 12-18 months, Gruber said, adding that agentic AI could go mainstream around the same time.
But defenders won’t be the only ones arming up. Attackers are increasingly leveraging generative AI for phishing, malware creation, and even polymorphic attacks that evolve in real time. As adversarial pressure builds, defenders may even have to think up out-of-the-box AI defenses.
As Kresten pointed out, “Offensive security may finally become an accepted thing. Businesses may be forced to adopt AI capabilities beyond what they initially considered safe and prudent to address these threats.” All in all, AI is no longer just the intern fetching logs–it is the caffeinated colleague who starts solving breaches even before you’ve hit snooze.
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