Abstract Security has introduced an AI-Gen composable SIEM built around streaming data, modular services, and AI embedded across the workflow. The design shifts SIEM from a single destination for logs into a control layer that decides what data is kept, where it goes, and how quickly it turns into action. For security teams, this is about reducing the amount of engineering work required to get to an investigation.
What changes in the day-to-day SOC
The biggest impact shows up in how analysts spend their time. Instead of maintaining pipelines, tuning ingestion, and constantly excluding low-value data, teams work from a smaller and more relevant dataset that is already shaped for detection and response.
Aaron Shelmire, co-founder and Chief Threat Research Officer at Abstract Security, told MSSP Alert that this directly affects team structure and scale. “This allows security teams to focus on the security work that's at hand, rather than getting bogged down in the data swamp. We’ve seen security teams gain the same visibility as teams with 5x the data engineering team members. By controlling the data up front with intelligent filtering, aggregation and routing, the security team doesn't have to exclude as much WORN (write once, read never) out data, and instead gets right to the security story told through the data.”
In multi-tenant MSSP environments, this model reduces the number of custom pipelines per customer and makes detections easier to standardize. Onboarding a new tenant becomes a data-routing exercise rather than a full deployment project, which changes both margin and time to value.
Adoption without a rip-and-replace
The platform is designed to run alongside existing SIEM and data lake investments. Most organizations start by connecting data sources to the new pipeline and continuing to feed their current tools. From there, they decide what data can be filtered, tiered, or removed based on actual usage and detection value.
Shelmire described the migration as incremental. “With a composable architecture, organizations can use the modules Abstract provides to bolt onto the chassis of their existing stack or replace it entirely. Most migrations we've run have dove right into connecting all data sources with Abstract, and then routing that data to multiple destinations, including the incumbent SIEM stack. Then filtering data out as customers become comfortable without. In other cases, we've typically tackled firewall, EDR, and netflow data to start, as those data sources provide the largest opportunities for reduction.”
This approach turns migration into a cost and performance decision instead of a platform deadline. It also allows teams to bring in data they previously could not afford to collect and apply detections to it immediately.
AI as part of the workflow, not an add-on
Abstract is positioning AI as something that depends on how data is prepared and scoped. When context is controlled at the pipeline level, AI can be applied to triage, investigation and response without the sprawl that comes from running multiple standalone models.
“Many companies are experimenting with bolting AI tools on top,” Shelmire said. “Sometimes this is done in house after an early POV shows value. Over time, many of these projects rot as model and prompt management become onerous. They also encounter issues as the project scales past 7-10 distinct agents, and artificial intelligence enters the dumb zone of too much context. By building workflows with AI inherently baked in, it allows us to have more precise results and stay within the smart zone of targeted and relevant context.”
The operational result is fewer disconnected AI projects and more consistent output from investigations and automated response.
Why the data layer now defines SIEM cost and SOC capacity
Security data volumes continue to rise as cloud adoption expands and AI-generated telemetry adds to log flow. In that environment, storage and processing decisions determine how much a SIEM costs to run and how quickly a team can respond. Running detections in the stream shortens the path from signal to action, while selective routing limits long-term retention in high-cost tiers.
For MSSPs, this directly affects service delivery economics. A standardized data pipeline across customers reduces the analyst-to-tenant ratio, makes outcomes easier to report and lowers the marginal cost of growth. For enterprises, it shifts SIEM planning from license sizing to data strategy. The immediate significance for buyers is the operating model: a SIEM that behaves like a configurable data layer, works with existing tools and uses AI in a controlled way. That changes how SOC teams scale, how services are priced and how quickly security results can be delivered.
