What Gigaom's New Research Calendar Covers

Gigaom has published a forward-looking research topic calendar rather than a conventional market report, laying out where its analysts intend to focus over the coming quarters. The notice, published on the site's research page, asks technology vendors to put their products in front of the analyst team through written input — explicitly stating that no meeting is required and that all written pitches are read.

The calendar covers seven emerging-technology areas: converged security operations bringing ingestion, threat hunting, orchestration, remediation and response into one place; storage layers underneath AI retrieval workloads; continuous discovery of fast-changing attack surfaces; raw-format storage meeting warehouse discipline and the lakehouse; real-time suggestion, generation and error detection inside the developer loop; containerized and Kubernetes application protection from development to runtime; and AI/ML/computer vision extracting structured data from unstructured documents at scale.

For vendors, the calendar also sets out what is already claimed and what remains open. Gigaom says its premier topics are set, while other parts of the quarter are still unclaimed or have nothing spoken for yet. The page references an October–December 2026 pitch window for some areas and a July–September 2026 window elsewhere, suggesting a rolling planning process rather than a single deadline.

Where Gigaom's Q4 2026 Questions Are Still Open

Converged security operations is the clearest research anchor

The calendar's most developed area combines ingestion, threat hunting, orchestration, remediation and response in one place. That framing signals Gigaom is looking less for point tools and more for platforms that can own multiple stages of security operations. Vendors in SIEM, SOAR, XDR and unified SecOps will likely be evaluated on how complete that loop is, not on a single feature.

The storage and lakehouse thread runs underneath the AI workloads

A distinct but related theme is the storage layer beneath retrieval: multi-dimensional data built for AI workloads and raw-format storage meeting warehouse discipline. Gigaom is asking where the lakehouse actually lands, meaning the analytical debate is moving from can you store it to can you serve retrieval and AI workloads from the same architecture. Incumbent data warehouses, lakehouse vendors and vector-database players all have a stake in that answer.

Developer-loop and Kubernetes security are still open ground

Two areas receive lighter definition: real-time suggestion, generation and error detection inside the developer's loop, and integrated protection for containerized and Kubernetes applications from development through runtime. The calendar says nothing is spoken for in these stretches, which suggests the analyst team has not yet locked its research questions. That is an advantage for vendors that can shape the evaluation criteria before titles and benchmarks are fixed.

Finally, the calendar confirms document AI is a named coverage area, with machine learning and computer vision pulling structured data from unstructured documents at scale. This sits alongside the other themes as a signal that Gigaom is treating unstructured data extraction as an enterprise infrastructure issue, not a niche automation problem.

How Vendors Can Shape the October–December Agenda

For technology vendors and research teams, the calendar functions as a low-friction entry point, but only if the pitch matches the named research questions.

  • If your product consolidates security operations — ingestion, threat hunting, orchestration, and remediation in one place — submit written input for the October–December 2026 window; Gigaom says it reads all written pitches and no meeting is needed.
  • For AI storage, vector database or lakehouse offerings, anchor your pitch on multi-dimensional data built for AI workloads and raw-format storage meeting warehouse discipline, since the firm says those questions are on the schedule but not yet written.
  • For container or Kubernetes security and developer-loop tools, move early; the notice identifies those areas as nothing is spoken for and among the easiest stretches to influence.
  • For document AI products, address how your ML or computer vision extracts structured data from unstructured documents at scale — one of the calendar's explicit coverage areas.
  • If you know a market is about to turn, send the correction now; the calendar explicitly says early input beats late corrections.