AI, SecDataOps, and federation are some of the primary forces that will continue to reshape security operations in the years to come. What specifically should we expect in 2026? We are still in January so I wanted to squeeze in my predictions for 2026 in the areas of my expertise!

As we look toward 2026, security data strategies — and the role of SIEM — are poised for big changes. The way security teams collect, store, and interrogate data has been on an unsustainable path for a few years now under the traditional SIEM paradigms. Rising costs, data sprawl, multiple cloud accounts and regions, and analyst overload have exposed the fundamental limits of centralized SIEM platforms.

Below are the five top predictions we expect to define security data and SIEM strategy in 2026.

1. With nextgen pipelining, cloud storage and data lakes will see more data growth than SIEMs.

Not all data belongs in a SIEM — nor should it. Historical logs, long-tail telemetry, and high volume sources are often expensive to centralize in a SIEM. Cloud data lakes and blob stores will become the replacement collection point. But pipelining needs to be fixed first for that to materialize. 

For pipelining, the new bar will be selective, reliable, optimal, and performant seeding. Legacy pipeline implementations continue to be engineering-heavy, brittle, and complex projects to set up and run. Security architecture will shift to a more selective and purpose-driven data pipeline strategy in 2026. In this model:

  • Security alerts continue to get sent to your SIEM for real-time analysis.
  • Voluminous logs flow into cost-effective cloud storage and data lakes. Long-tail and compliance data will live in searchable lakes or object stores.
  • Pipelining solutions that are reliable, easy to set up and manage (without needing engineering), and create an optimal gold layer of data, will get adopted.
  • Federated search will bridge across storage locations on demand.

This strategy aligns with a data mesh mentality where each data type is stored, structured, and queried based on its real business utility. For more on these aspects, refer to Query’s Security Data Pipeline solution.

2. The “SIEM as a lens” — not the system of record — will become part of SIEM migration strategies.

Traditional SIEM has long been anchored on centralizing all security data into one repository, but this model is breaking down under its own weight:

  • Skyrocketing ingestion and storage costs;
  • Data silos spanning SaaS, cloud accounts, network telemetry, and legacy systems; and
  • Analyst teams forced to withhold sources to control volume.

In 2026, SIEM consoles will become insight engines rather than data warehouses. The heavy lifting of data storage and retention will move to purpose-built stores — cloud object storage, lakes, and vendor-hosted native stores — while the new SIEM will focus on detection and visualization from that decentralized data.

This decoupling of console, collection, and storage mirrors how modern security data architectures are being reimagined. Refer to SIEM Migration for more on how transitioning from legacy SIEMs will go in 2026. Query has a Splunk app that lets you quickly move from the legacy “system of record” model to Splunk becoming the lens model, while preserving analyst workflows and operations to continue in Splunk.

3. Federated Search and Data Mesh will dent centralization as the core pattern

The days of forklift re-ingesting data into a monolithic SIEM are going to be gone. SecOps teams want access where the data lives — across multiple cloud accounts and regions, vendor SaaS platforms, legacy log stores, and modern data lakes — without duplication.

In 2026, federated search will be a foundational standard for querying security data. Analysts will no longer just think “ingest first, query later” — they’ll think “search everywhere now.”

This shift enables:

  • Real-time access to high-fidelity sources;
  • Cost-controlled storage strategies; and
  • Unified investigative context with no duplicate storage

Organizations that embrace federated architectures (i.e., security data mesh) will significantly reduce overhead while retaining investigative power. For more on these aspects, see my previous blog on Five Modifications to Imagine a New SIEM Architecture.

4. AI with Federated Intelligence will transform detection and investigation

The SIEMs of today rely on rules and content packs that assume all data is already centralized. But with data scattered across systems and formats, getting meaningful insight has become harder, not easier.

In 2026, AI will become the forcing function for a unified layer that interprets, correlates, and contextualizes security data across distributed environments.

Rather than manually stitching together queries, analysts will use AI-assisted federated detection logic to:

  • Generate cross-platform threat detection queries automatically;
  • Correlate heterogeneous schemas and data models – a Security Data Mesh and OCSF would be needed as the AI enablers; and
  • Surface insights that were not observable if your SIEM didn’t ingest that data.

This leap mirrors broader industry trends where AI is used to find meaning from data.

5. AI enablement is not just MCP and agentic. Common industry data models like OCSF will become key for AI understanding of security data.

As AI starts to get embedded into security operations, organizations will discover a hard truth: AI is only as good as the data it consumes. Simply exposing tools through MCPs, A2A and other agentic platforms is not enough. Beyond token limits over vast amounts of data, organizations will also realize that data is messy, fragmented, and semantically incompatible.

In 2026, the Open Cybersecurity Schema Framework (OCSF) will drive an edge for organizations seeking AI-driven security outcomes. Why? Because AI systems need:

  • Normalized and localized context across cloud, SaaS, identity, endpoint, and network data;
  • Consistent semantic meaning across data sources with predictable schema to reason, correlate, and learn; and
  • Reduce assumptions and hallucinations

Without standardized data models, AI is forced to constantly learn or assume structure instead of focusing on insight. OCSF shifts the problem left — enabling AI to reason over already-modeled relationships between events and entities containing relevant IT and security objects. This represents a fundamental evolution, i.e. from optimizing for human analysts to optimizing it for AI-assisted detection, investigation, and response. 

Individual security vendors will be late to support OCSF so that won’t happen in 2026. But you can use the Query Security Data Mesh as it’s the gateway to any vendor’s data converted to OCSF.

2026 Predictions Summary: 

The future of SIEM and Security AI is the adoption of distributed data with a federation tier.

The future of SIEM isn’t defined by bigger ingestion engines or heavier centralization. It’s defined by how well security data is structured, accessed, and understood — by both humans and machines.

In 2026, winning security teams will embrace:

  • SIEM as an insight layer, not a data warehouse;
  • Federated search over forced centralization;
  • AI as a native participant in investigations;
  • Purpose-driven security data pipelines; and
  • Open data standards like OCSF to make AI truly effective

At Query we believe the future of security operations and SIEM involves decentralized data, federated intelligence, and AI-augmented insight — not just bigger ingestion bills or heavier pipelines. This vision has guided our journey from day one: building security data platforms that respect where data lives, how it’s used, and how the next generation of SecOps will operate.

The future isn’t about moving all your data. It’s about unlocking all its value.If modernizing your security data strategy is on your agenda for 2026, let’s connect, our SecDataOps experts are standing by!