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264% ROI in new Forrester Consulting TEI study. 29 July 2026 Security teams using legacy SIEMs face rising data volumes, limited resources, fragmented tools, and growing operational complexity, while outdated architectures and pricing models make security data costly to collect, difficult to manage, and slow to investigate. Security analysts using legacy SIEMs often move between disconnected tools to piece together evidence while contending with a flood of false positives from poorly tuned detections. At the same time, engineers devote significant time and resources to maintaining brittle ingestion pipelines and parsers, while security leaders must decide which logs they can afford to retain and which visibility gaps they are willing to accept. A new Total Economic Impact(TM) study conducted by Forrester Consulting examines how the Gravwell Security Data Platform helped organizations overcome these constraints, reduced tradeoffs, and strengthened security operations with a more flexible, cost-effective approach to data ingestion, storage, and analysis. Gravwell commissioned Forrester Consulting to interview Gravwell customers and develop a composite mid-sized, regulated organization with four security analysts, tens of thousands of users, more than 9,000 endpoints and cloud resources, and hundreds of gigabytes of daily log volume. Forrester then evaluated the potential costs, benefits, flexibility, and risks of deploying Gravwell over three years. The study found that the composite organization achieved a 264% return on investment, $743,000 in net present value, and payback in less than six months. More importantly, Gravwell helped the security team investigate incidents faster, build better detections, reduce platform maintenance, streamline compliance reporting, and expand visibility without escalating ingest costs. 65% Faster Investigations, From Alert to Evidence Security investigations slow down when the evidence analysts need is scattered across identity, endpoint, SaaS, network, cloud, and infrastructure systems. Before Gravwell, customers interviewed by Forrester described manually gathering logs from multiple portals, requesting data from other teams, and correlating information by hand. Analysts could spend hours, or even multiple business days, collecting and validating evidence before reaching a conclusion. Gravwell centralizes security data in a time-series data lake, giving analysts a single queryable environment where they can move directly from an alert to the underlying evidence without switching tools or piecing together incomplete datasets. For the composite organization, Forrester modeled a 65% reduction in investigation time, enabling analysts to triage alerts, validate suspicious activity, and determine whether escalation was necessary far more quickly. This matters especially for false positives. Gravwell gives analysts the context to resolve them quickly, so low-value activity does not consume hours that should be spent on genuine threats. Build better detections, 70% Faster Legacy SIEM detection engineering often requires analysts to navigate proprietary rule formats, rigid schemas, indexes, preprocessors, and multiple configuration layers. That complexity slows the creation of new detections and makes existing logic difficult to tune. It can also force teams to rely on generic out-of-the-box content that does not reflect how their environment actually operates. Gravwell, with its lauded query capabilities, gives analysts a flexible workflow for creating and modifying detections. Logic can be reused, adapted, and applied across workflows without rebuilding an extensive processing chain. Forrester modeled a 70% reduction in the time required to create or materially modify detections. The benefit extends beyond speed. Customers reported that greater data access allowed them to build more precise detections, expand monitoring coverage, establish earlier thresholds, and reduce the number of irrelevant alerts and false positives reaching analysts. 90% Less SIEM Maintenance Means More Time for Security Before Gravwell, Forrester's composite organization spent 15 hours per week maintaining its legacy SIEM. That included managing ingestion pipelines, troubleshooting formatting problems, patching systems, maintaining schemas, repairing parsers, and responding whenever an upstream data source changed. Gravwell's structure-on-read architecture allows organizations to ingest data in its original form and structure it when queried. Teams do not need to normalize every source before the data becomes searchable. Forrester modeled a 90% reduction in ongoing SIEM administration effort as a result of removing much of the recurring pipeline, parsing, and mapping work associated with traditional SIEMs. Engineers can onboard diverse data sources more quickly and spend more time improving security operations and detections instead of keeping data flowing. Gravwell's Mission Support team also helps customers migrate data, replicate existing use cases, validate ingestion, and transition from legacy platforms. Customers described implementation effort as manageable and praised the hands-on support they received throughout onboarding. Compliance and Audit Made Easier with 80% Reduction in effort Compliance, audit, legal, and regulatory requests may be episodic, but they can consume substantial time when they arrive. Without centralized historical data, teams must coordinate across departments, export information from several systems, validate it, and manually compile it into a usable report. Due to cost, teams are often forced to minimize the time they retain data or move it to cold storage where it's more difficult to retrieve. Gravwell gives organizations a single source of truth for historical security data. Analysts can retrieve, correlate, validate, and export evidence through repeatable queries, dashboards, reports, and automated workflows. Forrester modeled an 80% reduction in effort per compliance, audit, legal, or regulated reporting request. Work that previously required hours of manual collection can be completed through a saved query or automated report, reducing disruption while improving the speed and consistency of the response. 60% Reduction in SIEM Cost Reduces ingest anxiety Many SIEM pricing models turn increased visibility into increased cost. As log volumes grow, organizations are forced to filter data, shorten retention, sample events, or exclude valuable telemetry altogether. Some customers interviewed by Forrester reported exceeding licensed event limits and dropping logs without knowing whether those missing logs contained critical activity. Gravwell removes the direct connection between data ingestion and licensing costs. Organizations can collect and retain the data they need without creating a larger bill every time data volumes increase. Forrester's composite organization replaced a legacy SIEM costing approximately $310,000 annually. The study modeled more than a 60% reduction in SIEM costs and nearly $694,000 in risk-adjusted present-value savings over three years. The result is a more predictable operating model that allows security leaders to plan around business requirements rather than ingestion thresholds. Give security teams room to scale By centralizing more data and reducing manual work, Gravwell helps security teams investigate faster, improve detections, reduce maintenance, accelerate compliance, and control costs without sacrificing visibility. The Forrester study shows how organizations can use Gravwell to strengthen and scale security operations on a single flexible data foundation. Download the complete Total Economic Impact(TM) study to explore the findings. Gravwell commissioned Forrester Consulting to conduct this Total Economic Impact(TM) study. The results are based on interviews with four Gravwell customers and the financial analysis of a composite organization. Results experienced by other organizations may vary.

From raw data to answers: meet the new Logbot. 26 May 2026 Today, Gravwell, Inc. is excited to announce a new and improved Logbot, Gravwell's AI-powered security data assistant, as part of Gravwell v5.9. The release adds deeper platform integration, faster natural-language investigation workflows, instant playbook and automation generation, and bidirectional AI integrations through MCP. These upgrades bring AI-driven assistance directly into daily workflows, helping practitioners get to answers faster and do more with complex security data. Security data is the backbone of today's security operations, but using it effectively at scale is a resource-intensive, time-consuming challenge for even the most seasoned practitioners. At the same time, the domain expertise required in SecOps creates a steep barrier for junior analysts to be impactful, making it no surprise that teams are turning to AI for support. Logbot is Gravwell's answer to those challenges, helping teams work through complexity faster and with less reliance on specialized expertise. Think of it as an embedded security guru that combines the capabilities of an engineer, architect, and analyst in one assistant to help users query, interpret, and act on security data faster. Logbot works with knowledge of your environment and knowledge base, along with broader security best practices, to help with tasks like query writing and working with frameworks such as OCSF. Writing accurate, meaningful queries, translating between query languages, or simply asking questions about your data or Gravwell cluster is as easy as using a natural language prompt. The result is a democratization of knowledge across the organization that drives better understanding, less manual effort, and a major boost in analyst productivity. Here's what to expect from the latest Logbot release: Deep Platform Integration:Logbot connects directly to live Gravwell systems via API and embeds into customer workflows through a natural chat interface. That means users can ask practical questions about their environment, such as "How is my cluster doing?" or "Where can I find this tag?", and get instant answers without digging through menus, documentation, or raw data. In this example, a customer asks Logbot to perform an audit of scheduled searches and alerts to make sure they're executing properly, in sync, and as expected. Logbot identifies enabled jobs that are not actually running, and surfaces the issue in a clear findings table that includes searches with a "Last Run" of never despite being enabled. Logbot checks scheduled searches and alerts, then flags critical issues where expected jobs are not actually running. After checking available flows and alert configuration requirements, Logbot enables the alert and scheduled search, confirms the consumer and schema details, and reports that both are now active. Logbot validates the dependencies needed to turn on alerting, enables the critical alert, then enables the scheduled search and confirms both are successfully running. Efficiency Gains: Logbot helps bridge the gap between raw data and understanding. It turns raw logs into actionable answers that speed up triage, reduce time spent searching for context, and help analysts respond with greater confidence. Logbot also generates playbooks and automations instantly, eliminating manual effort and reducing the need for deep technical expertise. In this example, a customer asks Logbot to create a full "Storage Expansion Action Plan" playbook with monitoring guidance and utilization thresholds. Logbot converts a simple request into a formal storage expansion playbook with decision criteria and operational guidance. The user's natural language prompt results in a structured playbook that recommends how to monitor storage growth and when to start planning for expansion, including utilization context, monitoring objectives, key metrics, and a recommended review schedule. The playbook captures current storage utilization, defines what to monitor, and outlines how to plan for future expansion. Logbot can also boost efficiency by quickly building, refining, and running queries to accelerate investigations, including translating queries from other query languages. This reduces reliance on query language expertise and shifts the focus from "how do I query" to "what do I want to know?" Users can use natural language prompts to translate queries from any query language into Gravwell query language or reconfigure queries to fit frameworks such as OCSF. In this example, Logbot uses saved search context to create a query for "User logged in" events and chart login counts over time that the user can copy and paste. This handy capability saves time and helps level-up more junior analysts who are not yet fluent in the query language. A natural-language prompt becomes a working Gravwell query that tracks user logins and visualizes them in a time-series chart. AI Tool Integration: Logbot functions as both an MCP server and MCP client, enabling bidirectional AI integrations across the security stack. In practice, that means teams can query Gravwell from other MCP-enabled tools or bring data from those tools into Gravwell to enrich investigations in one place. Logbot querying Gravwell through an MCP-enabled AI tool to check cluster health in plain language, showing how teams can bring live security platform context directly into their AI workflows. Logbot data is private and local: Logbot is based on open weight models hosted on Gravwell-owned infrastructure. Conclusion Logbot helps teams move from raw data to real answers faster, with less manual effort and less reliance on specialized expertise. By combining deep platform integration, natural language interaction, and private deployment within your environment, it gives practitioners a more accessible and efficient way to investigate, automate, and understand their security data. The new Logbot is another step toward making security operations faster, smarter, and easier to scale. Learn more about Logbot here, and take a deeper dive into Logbot on its docs page here.