--- title: "AI across the security lifecycle" page_name: "AI across the security lifecycle" type: "blog" slug: "ai-across-the-security-lifecycle" published_at: "2026-06-18" modified_at: "2026-06-18" url: "https://www.sumologic.com/blog/ai-across-security-lifecycle" canonical: "https://www.sumologic.com/blog/ai-across-security-lifecycle" markdown_url: "https://www.sumologic.com/blog/ai-across-security-lifecycle.md" lang: "en" excerpt: "Security teams have already layered AI into detection. Learn how agentic AI is closing the gap across the full SOC workflow and what your SIEM needs to support it." taxonomy_blog_category: - "AI" - "SecOps & Security" --- [ All blogs ](https://www.sumologic.com/blog "blog")[AI](https://www.sumologic.com/blog/ai), [SecOps & Security](https://www.sumologic.com/blog/secops-security) # AI across the security lifecycle [Michelle Beastall](#blog-author-block-376) June 18, 2026 4 min read [AI](https://www.sumologic.com/blog/ai), [SecOps & Security](https://www.sumologic.com/blog/secops-security) ##### Table of contents For nearly a decade, the security industry has used machine learning to solve detection. By feeding it enough logs and determining abnormal behaviors, it found the threats that rules-based systems miss. This delivered sharper anomaly detection, fewer false positives, and [UEBA](https://www.sumologic.com/glossary/ueba) is now essential. In fact, threat detection and analytics account for close to [44% of total SIEM spend, the single largest use case by far.](https://www.mordorintelligence.com/industry-reports/global-security-information-and-event-management) Using machine learning for detection was only the start. [Agentic AI](https://www.sumologic.com/glossary/agentic-ai) transforms SIEMs from embedded ML models to agentic systems that reason and act. Analysts remain, but they now enter later when most of the investigative groundwork is done. [Sumo Logic’s 2026 Security Operations Insights report](https://www.sumologic.com/guides/2026-security-operations-insights), a survey of 500+ IT and security leaders, shows SOCs evolving. While 96% say they’ve adopted AI, their use cases are concentrated at the most basic level of the workflow. AI can serve as the operational and decision-making backbone across the full lifecycle, from the first signal to resolution, but most organizations aren’t there yet. Here’s where that transition stands now, and what’s required to build a future-ready SOC. ## Where AI has already won The first wave of AI in security delivered on its promises. Anomaly detection sharpened, behavioral baselines augmented signature-based rules, and value is widely recognized: 90% of security leaders say AI/ML is extremely or very valuable in reducing alert fatigue and improving detection accuracy. In fact, nearly half of security leaders use AI for threat detection, but only 9% apply it to incident triage, the lowest cited use case across the entire SOC. But security operation centers still struggle with alert fatigue and false positives daily. The volume didn’t shrink because the attack surface kept expanding; cloud telemetry kept multiplying, and AI-accelerated threats kept raising the pace of incoming signals faster than detection improvements could offset them. Even though AI investment has been heavily front-loaded toward detection, that investment matters because detection quality directly sets the ceiling for everything downstream. The fidelity of detection determines how much investigative work lands on a human’s desk. ## The AI maturity arc Most SOCs fall into one of three stages of AI maturity. Where you sit today determines what you can realistically demand from your SIEM, and what you’re leaving on the table. ### Stage one: Cut the noise The first stage is operational automation, where you’re combining rules-based logic with ML to baseline behavior, suppress duplicate alerts, and normalize logs. This is table stakes, and the business case writes itself. You reduce volume, investigation time, and close more cases. The 2026 research confirms automation is now standard: 70% of security leaders say they’ve fully automated or mostly automated their threat detection workflows. ### Stage two: Guide the analyst The second stage is where AI starts to help with investigations. Analyst-assistive AI replaces static playbooks with systems that guide investigations in real time — natural language summarization, contextual risk scoring, and anomaly detection that hands analysts a “why this matters” narrative instead of a raw event stream. Only 37% of security leaders have a cloud-native SIEM with built-in AI features, unified telemetry, and scalability, and only 51% say their current SIEM is very effective at reducing mean time to detect (MTTD) and mean time to respond (MTTR). Stage 2 isn’t yet widespread, and the results reflect that. ### Stage three: Close the loop In the third stage, autonomous operation is introduced. This is [agentic AI](https://www.sumologic.com/glossary/agentic-ai), where systems don’t just analyze and recommend but reason across tools, correlate evidence, and act across the full SOC workflow without waiting for a human to advance each step. While stage two AI hands an analyst a better starting point, agentic AI enriches alerts with threat intelligence, maps activity to the kill chain, and initiates response actions autonomously. Few teams are here yet. Only 9% of security leaders currently apply AI to incident triage, the very workflows that agentic AI is built to transform, which illustrates exactly how much ground remains to cover. p. ## What to demand from your SIEM in the agentic era Not every SIEM is positioned to support agentic AI, and the architectural decisions organizations make now will determine how much you can benefit from agentic AI. Three things are non-negotiable. - **No vendor lock-in.** According to the report, most organizations evaluate vendors infrequently, and financial incentives to stay within a single vendor’s ecosystem are a primary reason. A platform that controls ingestion, analytics, and automation through proprietary tooling and closed APIs limits your ability to integrate the agentic capabilities being built across the industry. When the next generation of tools arrives, you need the architecture and the freedom to adopt them. - **Open data standards.** Agentic systems need to move freely across data sources. Platforms built on open standards like [OpenTelemetry](https://www.sumologic.com/glossary/opentelemetry) can collect, normalize, and route telemetry across environments without proprietary constraints. Closed ingestion pipelines are a ceiling on what any agentic layer can see and act on. - **Entity-centric detection.** Agentic AI reasons about what happened to whom, across what systems, in what sequence. Detection that correlates events back to a specific user, device, or service account gives that reasoning something coherent to work with. Without it, agentic investigation has nothing to act on. ## The SOC of tomorrow is being built now Detection was phase one, and the industry mastered it. But finding threats quickly only matters if the response is faster, and that’s where organizations still lose time. Analyst-assistive AI was phase two: smarter triage, guided investigation, faster mean time to respond. Most teams are somewhere in the middle of that transition today, as only half of security teams rate their current SIEM as very effective at reducing mean time to detect and respond, and just 52% are confident that their SIEM can scale to meet future needs. Agentic AI is phase three. Teams that have invested in the right foundations — open architectures, entity-centric detection, and no vendor lock-in — are positioned to adopt it. Those that haven’t will find their current SIEM sitting on infrastructure that can’t support what comes next. The organizations building the SOC of tomorrow are already choosing their platforms today. Ready to see how a flexible architecture works for agentic AI? [Get a demo.](https://www.sumologic.com/request-demo) ### Article Tags - [AI](https://www.sumologic.com/blog/ai) - [SecOps & Security](https://www.sumologic.com/blog/secops-security) Michelle Beastall Senior Product Marketing Manager Michelle Beastall is a Senior Product Marketing Manager at Sumo Logic, where she brings cybersecurity and SaaS products to life through clear, compelling messaging. With 15+ years in marketing roles and extensive experience spanning both established companies and startups, she has a passion for translating complex technology into stories that help businesses cut through the noise and make confident decisions. 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