AI tools are only as effective as the data they learn from. For the first time, the industry has tools to make this data accessible and meaningful. Let's look at how the industry sees AI SOC agents and how to maximize the use of their data sources.
This blog is part of a series on AI SOC tools in the cloud. Stay tuned for part 2!
There’s growing hype around AI SOC agents as a way to help security teams manage the overwhelming volume of alerts they face every hour. It’s a promising direction, and one that will undoubtedly reshape how SOCs operate.
In organizations managing cloud environments, adoption has been gradual. The challenge there doesn’t lie in the AI agents themselves, but rather in the data foundations these systems rely on.
(P.S. - Gartner’s latest Innovation Insight: AI SOC Agents (October 2025) report recognized Stream Security as an AI SOC tool, and notably, we’re the only Cloud Detection and Response (CDR) vendor mentioned in the report.)
AI SOC agents promise to make security operations faster. They use AI and large language models (LLMs) to automate alert triage, enrich investigations, and summarize findings. The goal: help analysts move faster so that security operations can scale in a sea of alerts.
Gartner’s AI SOC Agents report describes these platforms as augmentation tools designed to assist, not replace, analysts. They already handle tasks like correlation, prioritization, report generation, and summarization at scale.
AI is good at speed. It can process thousands of alerts, cross-reference multiple data feeds, and recommend next steps in seconds. Tasks that used to take hours now take minutes. For teams buried in alerts, that’s a big deal.
Most AI SOC platforms use the same data analysts already struggle with: fragmented logs and raw telemetry from SIEMs, EDRs, and cloud tools. They see what happened, but not why, how they’re connected, and how they impact one another.
Gartner notes that AI SOC agents depend on complete and context-rich data to perform well. Without it, they can make incorrect assumptions or hallucinate results. The report warns that measurable improvements only happen when the data foundation is reliable and connected.
Without good data, you’re just making decisions faster, not necessarily any better.
The data problem gets worse in the cloud. Visibility is spread across layers, including workloads, configurations, identities, and networks, that each produce different data formats at different speeds.
AI agents can’t fix that on their own. If the data isn’t collected or connected correctly, the AI (just like human analysts) never sees the full picture. It gets fragments: logs from workloads, alerts from native tools, maybe some identity data, but not how those pieces interact.
In dynamic cloud environments, that missing context means AI can’t reason about cause and effect. It can tell that something changed, but not whether it matters.
AI tools are only as effective as the data they learn from. For the first time, the industry has tools to make this data accessible and meaningful. Instead of bolting on tools that bring together insights that are by default fragmented, static, and prone to making mistakes, why not solve the challenge at the source?
Teams managing security in cloud environments need to work with data that’s connected across layers and constantly updated to reflect what’s actually happening. If we give AI tools the complete, accurate visibility needed to understand an environment, they’ll finally deliver on the promise of the AI SOC: not just faster operations, but better decisions.
Stay tuned for Part 2 of this series on AI SOCs, where we explore how complete, real-time cloud visibility with CDR natively builds AI into your SecOps.
Are you a Gartner client? Read the Innovation Insight: AI SOC Agents report here.
Stream.Security delivers the only cloud detection and response solution that SecOps teams can trust. Born in the cloud, Stream’s Cloud Twin solution enables real-time cloud threat and exposure modeling to accelerate response in today’s highly dynamic cloud enterprise environments. By using the Stream Security platform, SecOps teams gain unparalleled visibility and can pinpoint exposures and threats by understanding the past, present, and future of their cloud infrastructure. The AI-assisted platform helps to determine attack paths and blast radius across all elements of the cloud infrastructure to eliminate gaps accelerate MTTR by streamlining investigations, reducing knowledge gaps while maximizing team productivity and limiting burnout.
