Closing the Gap: How Automated AI Triage Transforms Cloud & SaaS Threat Detection

Automated AI Triage Can Transform Your SOC.

Security teams today face a fundamental imbalance: alerts are endless, but analyst time is finite. Every investigation eats away at valuable cycle time, leaving analysts focused on handling false positives or low-value noise rather than real threats.

The result? Burnout, backlogs, and missed true threats. And without live, enriched data behind alert decisions, triage becomes a guessing game rather than a reliable process.

Why Does Automated Triage Matter?

Traditional SOC workflows still look like this:

A detection fires → an alert is generated → it lands in an analyst’s queue.

Hours later, the analyst investigates, only to discover most are benign.

This manual, reactive model collapses in the cloud era, where ephemeral workloads, role chaining, and SaaS APIs produce thousands of signals every day.

Even in organizations where alerts are enriched automatically, the AI workflow is limited. Traditional automations today do not extend into the decision-making phase, meaning that bottlenecks and resource limitations are never truly resolved.

Teams are forced to choose: scale back coverage or drown in investigations.

AI Triage in Stream’s CDR Platform

At Stream, we’ve introduced fully Automated AI Triage: a capability that doesn’t just summarize alerts but makes a final decision using real-time cloud state, complete reasoning, and full context from the environment. When the AI Triage tool determines that an alert is benign with high certainty, it automatically closes it, so your analysts never even see it. For customers already using AI Triage within Stream, false positives and alert noise has reduced dramatically.

AI Triage changes the equation with four significant benefits:

• Reduces alert volume: Alerts confidently marked benign never reach the human queue.
• Saves analyst time: Analysts focus only on alerts that matter.
• Improves trust: Decisions are based on full real-time context, not partial logs. Without enriched, stateful data, even the most sophisticated of AI tools produces flawed verdicts.
• Delivers full coverage: Every single alert - critical, medium, or low - is reviewed by AI.

Nothing slips through the cracks.

How Stream’s AI Triage Works

‍

Unlike most AI SOC tools that rely on stateless SIEM logs or outdated CSPM snapshots, Stream takes a fundamentally different approach. Our AI-driven triage is powered by the CloudTwin™, a real-time digital twin of your entire cloud and SaaS environment.

This means your AI agent isn't guessing with partial or outdated data. It's working from a live, stateful, and accurate view of reality.

Stateful data gives AI models the best ability to triage detections accurately, working as the foundation that makes confident, automated decisions possible.

Stream was recognized in a recent Gartner® SOC Innovation Report on AI SOC tools that reinforced a critical truth: an AI agent is only as effective as the data it’s built on.

‍

And Stream takes this emphasis on data even further.

‍

‍

Data Retrieval

Stream's AI Triage agent cross-correlates CloudTwin context with all direct and indirect detections, linking signals across explotialiby possibility, blast radius, identities, workloads, and exposures to see the bigger picture. It understands relationships between API calls, configuration changes, and runtime behavior, giving it the same multi-angle reasoning a senior analyst would apply.

‍

The Dual-Pass Reasoning Approach to AI Triage

Each alert processed in Stream’s AI Triage engine goes through two distinct AI passes:

• Pass 1 – Breach Hypothesis: The AI tool assumes the alert represents a real compromise and builds a case for why it might be true, correlating CloudTwin data, identity chains, and related detections to explain potential impact and blast radius.
• Pass 2 – False-Positive Hypothesis: The AI tool then flips this perspective and argues why the alert is likely benign, referencing normal behavior, known baselines, or legitimate change patterns.

This two-pass approach replicates how experienced analysts actually think, creating balance that keeps the AI Triage agent unbiased. An AI agent biased toward marking alerts as benign will save time, but miss real threats. On the other hand, an AI agent biased toward flagging everything as suspicious will overwhelm your SOC with false positives. Therefore, Stream’s AI Triage agent works on a balance between both approaches.

Both hypotheses are then compared, and the system measures the deviation, which is a confidence gap between “breach likely” and “benign likely.” That deviation score determines whether the AI auto-closes the alert or escalates it for analyst review.

By forcing the system to argue both sides before deciding, we mimic the internal debate a senior analyst conducts instinctively.

‍

‍

The Impact for Security Teams

With automated triage, SOCs can see measurable improvements:

• ‍Fewer alerts, faster response. Teams move from drowning in 10,000 alerts to focusing on the critical few hundred that matter.‍
• Expanded detection coverage. Every detection, whether high, medium, or low, is analyzed. Nothing is ignored, not even the subtle signals that often precede breaches.‍
• Better analyst morale. No more endless false positives. Analysts spend their time hunting threats and building detections, not clearing queues.‍
• Time back for higher-value work. Automation gives teams the bandwidth to focus on active threat hunting, detection engineering, and continuous tuning, rather than triaging repetitive alerts.‍
• Smarter trust decisions. Each AI verdict comes with explanations and history demonstrating why it believed something was malicious or benign, allowing analysts to audit, trust, and improve the system.

Looking Ahead: AI in SecOps

AI has been creeping into security operations for years, but most vendors stop at summarization capabilities.

At Stream.Security, we go further: AI with reasoning, not just reaction. By running dual-pass evaluations, cross-correlating CloudTwin with detections, and confidently closing benign signals, Stream gives SOCs both precision and coverage at scale. Stream’s AI Triage engine was created with the goal of elevating and empowering security analysts, so they get time back to hunt, innovate, and strengthen defenses.

To learn more about Stream’s AI Triage Engine, book in a slot to speak with our team.

‍