What RSA 2026 Confirmed: The Agentic SOC Is Here

Twelve months ago, "agentic SOC" was a term you'd mostly hear from startups. At RSA 2026, it was the phrase on every major vendor's keynote slide. Google, Microsoft, CrowdStrike, Cisco, Arctic Wolf, Panther, and others all announced agentic security operations capabilities in some form. Even Accenture and Anthropic co-launched an autonomous security workflow offering. The convergence was fast enough to make the product category feel settled overnight.

But convergence on a label isn't the same as convergence on a capability. And for security leaders now tasked with evaluating these platforms, the shared vocabulary actually makes the job harder, not easier.

When Everyone Claims the Same Capability, the Label Stops Being Useful

A year ago, the evaluation question was straightforward: should we adopt AI-driven investigation in the SOC? That question is answered. The market has moved on. Every major platform vendor, MDR provider, and SIEM company is shipping some version of agentic security operations.

The problem is that "agentic" now covers an extremely wide range of implementations. Some of these platforms dynamically plan and execute multi-step investigations across your full tool stack. Others run a fixed enrichment sequence on a single vendor's telemetry and present a confidence score. Some are essentially chatbots that build workflows when prompted. All of them get called "agentic." All of them show up in the same analyst reports and conference sessions.

For buyers, this means the category name is no longer a useful filter. You need to evaluate what's underneath.

Four Questions That Separate the Real from the Rebranded

Does the platform actually do the work a analyst would do?

This is the question that should come before everything else, because it determines whether the platform creates real operational value or just creates a different kind of work.

A meaningful AI SOC platform should eliminate manual investigation effort that your team is actually spending time on today. That sounds obvious, but the bar is higher than it appears. A system that surfaces a confidence score and a summary paragraph hasn't eliminated work; it's given an analyst a starting point and left them to do the investigation. A system that requires prompting to function has shifted the work from investigation to prompt engineering. A system that runs surface-level enrichment against a single data source has done the easy part and left the hard part to your team.

The threshold we think matters: can the platform match the depth, quality, and accuracy of an elite analyst's investigation? Not an average investigation. Not a triage-level assessment. The kind of thorough, multi-source, documented investigation that your best analyst would produce if they had unlimited time on every alert. If the platform can do that autonomously and accurately, it's creating real capacity. If it can't, you're buying a tool that still requires significant analyst time to be useful, and the ROI case gets harder to make.

When evaluating, ask to see completed investigations and look at what actually happened. Did the platform query the relevant data sources? Did it follow the evidence through pivots the way a human investigator would? Did it reach a conclusion that's supported by the evidence trail, or did it pattern-match to a disposition? And critically: what is the accuracy rate across thousands of production investigations, not a curated demo set?

Prophet AI's approach is to produce a complete, auditable investigation for every alert, matching the methodology a senior analyst would follow, documented at every step, with measurable accuracy across production environments. That's the standard buyers should demand from any platform in this category, regardless of vendor.

Can it reason across your full tool stack?

Several of the RSA announcements reinforced a pattern that should concern multi-vendor environments. Most of the newly announced agentic capabilities operate primarily within the announcing vendor's own ecosystem. An endpoint vendor's AI investigating that vendor's alerts is useful but limited. A SIEM vendor's agents operating within that SIEM's data is a start, not a solution.

A meaningful agentic investigation often starts in the SIEM, pivots to the EDR for endpoint context, checks the identity provider for authentication history, and pulls cloud API logs to establish whether a lateral movement pattern is real. If the platform can only see what its parent vendor's tools show it, the investigation will be structurally incomplete for any organization running a heterogeneous stack, which is most of them.

The evaluation question is straightforward: hand the platform an alert that spans three or four of your tools and see whether it can investigate it end to end without manual intervention. An identity alert that requires correlating Entra ID sign-in logs, EDR process telemetry, email activity, and cloud API calls is a good test case. If the platform can only query one of those sources natively and needs you to copy-paste context from the others, you're looking at a single-vendor assistant, not an agentic investigator.

What does vendor lock-in actually look like here?

This is the question that doesn't come up often enough. When a platform vendor builds agentic AI capabilities, the natural engineering incentive is to optimize for that vendor's own products first, second, and always. The integration depth will be best with their own tools. The investigation logic will be tuned for their own telemetry formats. And critically, the incentive to deeply support a competitor's product in their investigation workflow ranges from low to nonexistent.

This matters because the average enterprise security stack includes tools from multiple vendors, and that isn't changing. If your AI SOC platform is built by your EDR vendor, ask how thoroughly it investigates alerts from a competing EDR, or from a SIEM it doesn't own, or from a cloud security tool that competes with its own offering. Ask whether those integrations get the same engineering investment, the same investigation depth, and the same update cadence as the vendor's own products.

A platform vendor's agentic AI will always work best within its own ecosystem. That's not a criticism; it's a structural reality. But it means that for organizations with heterogeneous tooling, a vendor-neutral AI SOC platform that has no competitive incentive to deprioritize any data source may deliver more consistent investigation quality across the full stack.

Does investigation data improve detection?

This is where the RSA announcements got interesting. Several vendors positioned closed-loop detection tuning as a core feature. The idea that investigation outcomes should drive detection improvements is gaining traction across the category, and rightly so.

Agentic SOCs offer the opportunity to continuously improve detection quality and coverage through a feedback loop. Prophet AI’s architecture is designed around this loop. AI SOC Analyst generates investigation data. Detection Advisor uses that data to optimize the detection surface. When the Detection Advisor identifies a gap, it feeds a prioritized hunt hypothesis to Threat Hunter. Findings flow back to build permanent detections. The investigation, detection, and hunting layers reinforce each other by design.

Where This Leaves Evaluation

The gap between demo and deployment is where the evaluation gets real. A platform that has been running production investigations for a year, with measurable accuracy data across thousands of investigations, is in a fundamentally different position than one that announced agentic capabilities this quarter. That doesn't mean the incumbents won't catch up; they probably will. But the organizations making decisions now benefit from evaluating platforms against production evidence, not roadmap promises.

The agentic SOC category is validated. RSA 2026 settled that. What's harder now is distinguishing platforms built from the ground up for autonomous investigation from those that added an "agentic" label to existing capabilities in the last two product cycles. The four questions above, investigation depth and accuracy, cross-tool reasoning, vendor lock-in incentives, and the investigation-to-detection feedback loop, are the filters that matter.

The vocabulary has converged. The implementations haven't.

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