Detection engineering is getting a very well-deserved moment in the limelight. The 2026 Gartner Hype Cycle for Security Operations introduced a new Detection Engineering Automation Solutions (DEAS) category for “products and services that facilitate developing, testing, and improving threat detection content” and provide detection posture management capabilities.
Of course, we’re very familiar with this corner of the cybersecurity market. We’ve felt for years that detection automation deserves dedicated analyst coverage, so it’s nice to see Gartner catching up and giving detection engineering its due. Buenos DEAS!
So let’s unpack some of the more interesting tidbits from the research, discuss coverage of adjacent categories, and tee up some takeaways.
Finger on the (Innovation) Trigger
As a quick level set, Gartner’s hype cycle framework maps a product category’s expectations (and implicitly, value) over time. The cycle starts with the “innovation trigger” and ramps up the “peak of inflated expectations.” Then it crashes down to earth into the “trough of disillusionment,” before hitting its stride into the “slope of enlightenment” then finally delivering consistent value at “the plateau of productivity.”
Gartner positions DEAS firmly in the “innovation trigger” stage. This placement signals that detection engineering has gained enough critical mass to merit dedicated tooling to support it. It’s no longer a niche role for the top 1% of orgs, or an annoying ad hoc task for overworked analysts. Detection engineers play an essential role in the SOC, and vendors are innovating to help broaden and amplify their business impact.
Overcoming Operational Challenges with Best Practices
We’ve covered detection engineering’s significant operational burdens, and Gartner also acknowledges its labor-intensive nature. They point out that AI augmentation from DEAS frees up experts for more impactful strategic tasks, and that’s a key premise behind our Agentic Fleet: automate toilsome manual efforts, scale your capacity without adding headcount, and focus on optimizing your overarching detection posture and strategy.
One strategic concept that optimizes your detection posture AND resonates strongly with our approach to detection tradecraft is Gartner’s framing of “antibrittle” detections. What they call antibrittle, we’d simply call durable: rules that remain robust and effective in the face of different attacker tactics and technical tooling. This approach to detection development involves anchoring detection logic on immutable artifacts, focusing on identifying the underlying fingerprint of the adversary’s behavior, not the wrapper or tooling surrounding the behavior. Check out our Pursuit of Immutable Artifacts blog and webinar for an in-depth overview of our philosphy.
The Long Road to Maturity (or, The Short Road to Consolidation)
Gartner projects 5-10 years until the DEAS market reaches the “plateau of productivity.” If you’ve been following us since our founding, another 5-10 years to maturity might seem too long, but relatively few categories reach Gartner’s promised land of productivity anyway. Most get consolidated into broader categories, repackaged into new categories, or rendered “obsolete before plateau” (oh, hi there, XDR and SOAR!). It will be interesting to see how consolidation plays out here, since larger platform vendors are already bundling these capabilities into their offerings and continuing to expand their OOTB content.
We believe that a dedicated, neutral detection layer that unifies visibility of coverage across SIEM, EDR, and other detection tools is the best way to evaluate detection posture and pinpoint gaps. But we also see the importance of integrating directly with other components of the stack, especially components to the “left” of detection. And that brings us to the data layer.
Data Lakes & Pipelines as Detection Gold Mines
Embedding detection strategy into the operational realities of data pipelines and data lakes increases the impact of both data and detection engineering, so it’s worth looking at Gartner’s coverage of those two markets.
Observability Pipelines: Off the Cycle, but Still on the Hype Train
Curiously, Gartner removed observability pipelines from this year’s hype cycle entirely, citing its closer alignment with broader IT monitoring disciplines. This feels somewhat counterintuitive. Pipelines’ collection, enrichment, filtering and routing capabilities are incredibly valuable for SecOps in general, and detection specifically. And as security-relevant telemetry volumes continue to explode, this category’s value will only increase.
The more convincing rationale for its removal is market consolidation. A wave of acquisitions last year saw Onum get absorbed into CrowdStrike, Chronosphere into Palo Alto Networks, and Observo.ai into SentinelOne, folding their pipeline features into much larger platforms. Ironically, orgs that initially chose those pipelines to move data freely throughout their stack are likely feeling pressure (via bundling incentives and additional volume discounts) to move more data into the larger platform’s data infrastructure… potentially creating the vendor lock-in they sought to avoid. Now only a few independent pipeline vendors remain. Cribl is the most significant, effectively creating the category in 2018, and we’ve partnered with them to deliver detection visibility and coverage at scale.
For Gartner, it makes more sense to cover markets with a significant number of vendors with point solutions that can be analyzed independently from the broader platform feature set. We get why Gartner dropped it, but since security data pipelines and detections are so intertwined, we’ll be watching this space with great interest.
Data Lakes for Flexibility & Efficiency
Security Data Lakes (SDLs) are another crucial link in the security value chain, and Gartner’s coverage of this category echoes important points from the DEAS research on cost and efficiency.
They mention how DEAS enables informed, transparent decisions on detections that help optimize costs, while SDLs provide cost-effective alternatives to SIEM platforms for more flexibility around ingestion and storage. As legacy SIEM data management limitations become increasingly apparent, SDLs help keep costs in check while increasing the telemetry available for analysis and expanding the scope of their detection programs… which in turn will increase the need for DEAS functionality.
The recurring theme here? These tools extract more value from security data and detection for more efficient programs. Together they create higher fidelity signals that help the SOC defeat adversaries. And again, these points resonate with our view that security data architectures that treat detection as a first class citizen will unlock more transformational SOC outcomes
Cautions as Takeaways
Let’s wrap up with some takeaways related to cautionary tales in Gartner’s research.
When Tools Alone Can’t Overcome Skills Gaps
Gartner points out a key driver for DEAS: bridging detection engineering skills gaps by “making sophisticated detection engineering workflows accessible to existing security teams.” Detection engineering is a highly specialized (and expensive) skill set, so the power of DEAS comes from giving organizations detection support as they expand their maturity and scope.
But a word of caution: if your existing security team doesn’t include anyone with detection engineering expertise and focus, DEAS tools will not magically auto-pilot your detection program to success. They still need care and feeding by a dedicated practitioner, ideally focused specifically on detection. Later on, Gartner includes a recommendation to “establish a formal DE practice, including a robust and systematic process for developing threat detection content, before investing in specialized tools.” And we couldn’t agree more.
When “Good Enough” Isn’t Actually Good Enough
We’ve talked about larger security platforms offering a subset of DEAS features, but let’s put a finer point on that to close this out.
Gartner notes that many larger vendors bundle DEAS components within their broader platform, which might discourage organizations entrenched in the platform vendor’s ecosystem to adopt DEAS tools. But note that the actual detection engineering functionality is quite limited with bundled solutions. Meanwhile, OOTB detection content and native rule management capabilities from SIEM platforms may feel sufficient, but they are also limited in scope and depth. They’re likely to be biased toward their platform’s features and workflows, potentially restricting your view of what’s possible for your detection programming.
So when looking at the larger platforms’ built-in DEAS capabilities, you might think they’re good enough… but are they actually? They might get you from nothing to something, but they’re less likely to get you to the highest levels of detection engineering tradecraft.
Gartner puts it simply: native features may feel sufficient, “making (organizations) reluctant to invest in specialized DE solutions–not realizing that this can limit the maturity of DE practice.” And again, we couldn’t agree more.