Security operations have been running on the same basic model for years: wait for an alert, investigate what happened, and decide what to do next. That model is starting to break under the weight of modern environments. Too many signals, too much context to stitch together, and not enough time to keep up with how quickly threats evolve.
Dataminr’s latest launch, Dataminr for Cyber Defense, is built around a different idea: move the decision point earlier by connecting external threat signals, internal telemetry, and business risk in real time.
From investigation-heavy workflows to faster decisions
Most SOC workflows today still rely on a sequence that hasn’t changed much. An alert fires, the analyst pivots across tools to understand the threat, checks whether the organization is exposed, and then decides how to respond.
Dan Cole, VP of Product Marketing at Dataminr, explained to MSSP Alert, “Today, most SOC workflows go: alert fires in the SIEM → analyst pivots to a TIP or threat feed to understand the threat → opens three more tabs to check if they're exposed → manually correlates what they find → decides whether to act. That process takes hours per alert.”
What Dataminr is trying to do is compress that entire process. Instead of forcing analysts to gather context manually, the platform assembles it upfront and delivers it inside the tools they already use. That changes the workflow in a simple but meaningful way: less time spent figuring out what’s happening, more time deciding what to do.
“We're not replacing an organization’s SIEM. We're replacing the hours of manual work their analyst does after the SIEM fires,” Cole adds.
Starting points vary, but the direction is consistent
The platform is structured around three core capabilities: real-time threat intelligence, agent-driven intelligence operations, and predictive exposure management. Each one targets a different operational gap, which reflects how uneven security maturity tends to be across organizations.
“There are three entry points, and it depends on where the pain is sharpest,” Cole says.
For teams overwhelmed by alerts, the focus is on filtering and prioritizing signals in real time. For organizations building out threat intelligence programs, the emphasis shifts to automating how intelligence is produced and shared. And for leadership teams, exposure management connects security posture to financial impact, which is increasingly what drives decisions at the board level.
The important detail is that these aren’t rigid stages. Organizations can start where they need to and expand over time.
Where this fits in an existing SOC stack
For MSSPs and enterprise SOCs, the immediate question is where this sits alongside existing tools. The answer is less about replacement and more about how intelligence flows through the stack.
“The right framing is a force multiplier,” Cole says.
Dataminr sits upstream of systems like SIEM and SOAR, feeding them with earlier and more contextualized intelligence. That includes pushing indicators into correlation rules, triggering automated playbooks, and adding business risk signals into incident workflows. The goal is to make existing tools work more effectively, rather than adding another layer to manage.
In multi-tenant MSSP environments, scale becomes the real test. Tailoring intelligence for each customer often creates operational overhead. Dataminr’s approach is to automate that process so relevance is handled per client without manual tuning.
“This is the right question, and it's why we built client-tailored intelligence as an automated capability rather than a manual configuration exercise,” Cole notes.
That matters for service providers trying to standardize workflows while still delivering customer-specific outcomes.
AI that sits inside the workflow
A lot of security vendors are now talking about agentic AI, but the differences tend to show up in how deeply AI is embedded into operations. In this case, AI is positioned as the system that drives detection, correlation, and prediction, rather than something layered on top.
“Remove Dataminr's AI, and there is no product. The AI detects, assembles, tailors, and predicts. It's the engine,” Cole says.
That distinction matters because it ties AI directly to operational outcomes. If it’s working as intended, teams should see changes in investigation time, response speed, and analyst workload, not just better summaries or dashboards.
Why this shift matters now
The broader shift here is about where security decisions happen. For a long time, those decisions have been made after alerts surface. What Dataminr is pushing toward is a model where decisions happen earlier, with clearer context about exposure and business impact.
For MSSPs, this changes how services are delivered and measured. The focus moves toward prioritizing risk across environments, standardizing response, and demonstrating outcomes that tie back to business impact.
Concepts like continuous exposure management and unified risk intelligence are now moving into real deployments. And as that happens, expectations change. It’s no longer enough to detect and respond quickly. The pressure is to understand what matters sooner and act before incidents take shape.
