Artificial intelligence is fundamentally changing how security teams detect and hunt cyber threats. By analyzing massive volumes of security data, AI helps uncover subtle indicators of malicious activity and identifies potential attacks far faster than traditional tools or human analysts working alone.
Gartner predicts that by 2028, half of all threat detection, investigation, and response (TDIR) platforms—including EDR, XDR, SIEM, and SOAR solutions—will incorporate agentic AI capabilities, up from less than 10% today. The firm believes AI will strengthen threat detection, incident response, and containment while helping security teams address persistent skills shortages.
Processing telemetry at unprecedented scale
AI's biggest impact on threat detection stems from its ability to process telemetry at a scale human teams simply cannot match, according to security experts.
Modern IT environments generate billions of logs and events daily across endpoints, networks, cloud services, and identity systems. Machine learning models correlate these signals in near real-time, identifying behavioral anomalies—unusual login patterns, suspicious lateral movement, data exfiltration attempts—that would otherwise disappear into the noise.
Enterprise security teams expect these capabilities to significantly strengthen their detection posture. In a 2025 survey conducted by Anvilogic and the SANS Institute, 45% of respondents said their organizations have already integrated AI into threat detection workflows, while 88% believe AI will play a major role in detection engineering within three years.
Organizations are using AI to automate routine tasks traditionally handled by Tier 1 and Tier 2 analysts, says Martin Sordilla, senior technology and security architect at Accenture. This work includes reviewing logs, triaging alerts, identifying indicators of compromise, correlating events, and contacting system owners during investigations. AI accelerates these processes by automating alert triage, documentation, evidence collection, and chain-of-custody tracking.
Organizations are seeing efficiency gains of roughly 40-50% for lower-tier SOC tasks, freeing human analysts to focus on advanced investigations and response activities, Sordilla says.
Cutting through alert fatigue
AI agents are reducing alert fatigue by clustering alert patterns and enabling risk-based prioritization, says Dipto Chakravarty, chief product and technology officer at Black Duck.
Natural language processing agents can summarize threat alerts at scale and correlate them with threat intelligence feeds such as CVE.org and the CISA KEV Catalog, he explains. Incident response workflows benefit from automated playbooks for common incidents, while AI agents enrich threat intelligence by ingesting and correlating data from multiple sources, adding context such as CVE information.
"AI agents today can effectively accelerate derivation of insights from organized and normalized datasets" by allowing analysts to ask questions in natural language, says Nicole Bucala, CEO at Databee. They eliminate the need for specialized queries, analytical dashboards, or manual analysis.
Rather than flooding analysts with thousands of low-confidence warnings, AI-enabled detection platforms score and correlate alerts, group related activity into higher-fidelity incidents, and filter out routine behavior. The result is less alert fatigue and a shift from manual triage toward deeper investigation and response.
"AI is helping SOCs escape 'activity theater' by turning raw noise into faster, higher-confidence decisions backed by evidence," says Craig Jones, chief security officer at Ontinue.
SOC burnout is driven by alert volume, fragmentation, and ambiguity—pressures that exist for any team operating at scale, Jones notes. Analysts spend too much time working through high-volume, low-signal alerts and context-switching across multiple tools just to assemble basic investigation details.
Faster containment with fewer errors
The real value of AI isn't processing more alerts or closing more tickets—it's containing real threats sooner with fewer mistakes, Jones says.
"When AI is used to correlate weak signals into coherent incidents, enrich investigations automatically, and recommend safe next actions inside clear guardrails, you stop measuring effort and start proving outcomes," he explains.
Security experts expect AI to reshape the skills needed in security teams. Rather than eliminating jobs, it will help automate routine tasks and shift roles toward engineering and system design, Accenture's Sordilla says. The traditional SOC analyst role—focused on manual log review—is evolving into security engineering roles centered on building resilient systems, automation pipelines, and AI-assisted defenses.
Early data shows measurable gains for organizations deploying AI for detection engineering. In a Google study of 3,466 senior leaders, nearly 67% of early agentic AI adopters reported positive impacts on their security posture. Of this group, 85% said AI improved their ability to identify threats. Early adopters are seeing quantifiable benefits in both efficiency and efficacy.
Still, experts caution that AI-driven detection isn't a silver bullet. Adversaries are increasingly experimenting with AI themselves—using it to generate convincing phishing campaigns, automate reconnaissance, or modify malware to evade signature-based defenses. This dynamic pushes defenders to treat AI not as just another security tool, but as part of a broader evolution where human expertise, threat intelligence, and machine learning must work together.
"Cyberattacks have been industrialized at machine speed," says Ram Varadarajan, CEO at Acalvio. "We need to respond in kind."
That means implementing defensive AI that handles high-volume technical tasks such as triaging phishing emails, analyzing massive network logs for behavioral anomalies, deploying AI-aware cyber deception, and autonomously quarantining compromised endpoints to prevent lateral movement.
"When it's a machine-speed AI attacker, no human will ever be able to keep up, and these complex AI attacks are going to be launched at scale," he notes.
Getting AI implementation right
The key to extracting value from AI in threat detection is keeping humans in the loop. Any threat finding or remediation action based on AI insights—especially those with nontrivial consequences for business operations—should remain under human oversight, says Databee's Bucala.
"Human in the loop is the mantra," she says. "There's a lot of business risk that can be incurred through full automation unless the margin of error in machine-made decisions is close to zero."
While AI shows promise in threat detection, it still needs refinement. Organizations should establish processes that include human validation by people with the attention to detail and context needed to spot-check AI summary results and decisions, Bucala notes.
AI is not a substitute for basic security hygiene, adds Accenture's Sordilla. If an organization has weak security practices, AI may simply accelerate existing problems. Companies should first ensure they have strong governance, clear security standards, and mature processes—such as those outlined in frameworks from NIST and the International Organization for Standardization—before layering AI into their security programs.
"AI is a force multiplier," Sordilla says. "If your company is heading in the wrong direction, you are going down the drain faster" by deploying AI incorrectly.
