RSAC 2026 made one thing very clear to me: the market is moving fast, but it is still deeply confused. The big announcements from Google, Splunk, and Databricks all point in the same direction. Security operations are becoming more agentic, more API-driven, and more automated. But most of the category still looks crowded, early, and only lightly differentiated.
The interesting part is not that everybody now has an AI story. It is where the pressure is landing: attack speed, active response, and the possibility that AI itself becomes the primary user of the security stack.
TL;DR
Attacks are now fast enough that human-speed response is no longer a sufficient default.
That will push the market toward active response, which is useful but also dangerous if the control logic is not deterministic enough.
Most AI SOC vendors still sound similar because many of them sit on top of existing SIEMs and alert streams rather than changing the underlying detection or data architecture.
The big SIEM vendors are moving, and one major EDR/SIEM vendor is expanding AI security into on-prem and sovereign environments.
If AI becomes the user of security products, the UI matters less, the API matters more, and the economics of expensive SIEM platforms get harder to defend.
Attacks are getting faster
This is the part of the market I think people are still underestimating. CrowdStrike’s 2026 threat report says the average eCrime breakout time dropped to 29 minutes in 2025, and the fastest case it observed was 27 seconds. Databricks used its Lakewatch announcement to make a related point from the vulnerability side, citing research that mean time to exploit has fallen from 23.2 days in 2025 to 1.6 days in 2026.
That changes what matters in the SOC. A lot of SIEM workflows still assume there is time to search, enrich, discuss, and decide. That model was already strained. It gets worse when attacks speed up and when the adversary is using AI to compress its own loop. Search still matters, but a search-centric operating model is not enough if the environment can be compromised end to end in under an hour.
The obvious answer is more active response. The problem is that this is where things get dangerous. If teams start handing more containment and remediation decisions to AI before the systems are ready, we are going to see more self-inflicted outages. The market is moving there anyway, because the alternative is to keep defending at human speed against machine-speed attacks. SOAR was supposed to close part of that gap and clearly did not.
AI SOC is still confusing and mostly sounds the same
That was probably my main emotional reaction leaving RSAC: confusion. There were simply too many vendors with very similar messaging. RSAC says the conference had more than 600 exhibitors this year. I could not independently validate an exact count of 36 AI SOC vendors from public RSAC data, but “roughly three dozen” felt directionally right from the floor, and many of them sounded remarkably similar.
The common pitch was familiar: reduce alerts, triage faster, investigate faster, give the analyst a copilot, automate parts of response. Some of that is clearly useful. But a lot of it still feels like a layer on top of the existing SIEM rather than a rethink of the detection stack itself. If the AI mostly sits on top of alert streams coming out of a legacy backend, then it may improve analyst productivity without materially fixing false negatives, brittle detections, or poor data design upstream.
That is also why I do not think most of this market is really using LLMs in a deep way yet. In most cases, the models are being used for triage, recommendations, summarization, and analyst assistance. That is very different from using LLMs for real detection, broader SOC operations, or meaningful changes to the underlying architecture.
That is why so much of the category feels undifferentiated. The interfaces are different, the branding is different, and the demo flows are different, but the center of gravity often looks the same. The latest platform announcements only reinforce that point. If the platform owner adds the agentic layer too, the vendors sitting on top of Chronicle, Splunk, or similar platforms have a much harder moat to defend.
The architecture is shifting
By this point, the vendor movement is established. The more interesting question now is what it does to architecture. SentinelOne adds another signal here by pushing more AI security capability into on-prem, sovereign, and air-gapped environments.
Put together, that points to a broader market shift. Storage matters more. Data routing matters more. Sovereignty and local control matter more. Cheap data lakes, strong analytics layers, and flexible orchestration matter more. Traditional SIEM UI matters less than it used to, and that matters not just for SIEM vendors but also for MDRs that differentiated by putting an AI layer on top of someone else’s backend.
That is also why Splunk’s cost model keeps coming back into the conversation. Splunk is powerful and mature, but if the agent becomes the main consumer of the system, customers start asking a different question: am I paying for the analytics engine, or am I paying for UI, workflow, and operating complexity that an agent increasingly does not care about?
If AI becomes the user, the stack changes
The most important implication may be economic, not just operational. Security products were built for human analysts. The value lived in the UI, the workflow, the search language, the dashboard, and the services needed to make all of that usable. But what happens if the real user becomes Claude Code, Codex, Gemini, or some internal agent instrumented across the entire security stack? Daniel Miessler has been arguing that companies and products increasingly become APIs. Security looks like one of the clearest versions of that shift.
In that world, every product starts to look more like an API than an application. That is exactly where the recent announcements are heading. LimaCharlie’s new lc-soc release is a concrete implementation of the same idea: an open-source “agentic SOC as code” where AI agents are coordinated through the cases system and D&R rules, then deployed and versioned like infrastructure.
If AI becomes the primary user, the UI does not disappear, but it stops being the center of gravity. The agent does not care about your console. It cares about whether the data is accessible, whether the schema is consistent, whether the analytics layer is fast, whether the permissions model is clean, and whether the actions are safe to orchestrate.
That creates real pressure on expensive SIEM economics. If the agent can query multiple tools directly, the premium attached to a deeply monetized UI gets harder to justify. The market may move toward something simpler: cheap storage, a strong analytics layer, and an orchestration layer on top. That does not mean incumbents disappear. It means their value proposition changes. If AI becomes the user, the winners may be the vendors with the best APIs, control points, and data access model.
Evals become part of the control layer
The next problem is trust and determinism. Once you push AI beyond triage and recommendations and let it make or recommend more consequential changes, you need a way to keep the system reliable. That is where eval loops come in.
I heard Josh Saxe make this point at RSAC in the context of AI-first infrastructure management: if agents are going to make changes in live systems, you need strong evaluation around them to keep behavior bounded and repeatable enough to trust. I think the same logic applies directly to security operations. The market is moving toward active response, but the models themselves were not built around strict determinism.
That means the answer is not blind autonomy. It is more likely a layered system where adaptive AI sits inside clearer control boundaries, with evals, policy, and deterministic automation around it. Evals stop being an AI engineering detail and become part of the security control layer itself.
I recently joined Tim Peacock and Anton Chuvakin on the Google Cloud Security Podcast to talk about SIEM, AI SOC, pricing, federated architecture, detection engineering, and why network telemetry is quietly becoming important again.
The short version is simple: SIEM is not dead. Calling it obsolete makes for good marketing, but it is not a serious thesis.
The new wave of AI SOC, SIEM, and pipeline vendors is not proving SIEM is dead. It is proving SIEM vendors left too many gaps open for too long.
The recent wave of AI SOC startups, pipeline vendors, and new SIEM entrants is a response to real pain in the market. They are not replacing SIEM. They are capitalizing on the gaps incumbent vendors left open.
TL;DR
SIEM is not dead. Vendors just left too many gaps open.
AI SOC often exposes those gaps more than it replaces SIEM.
Alert reduction alone will hide false negatives.
The real fixes are better routing, detection, context, and workflows.
Network telemetry still matters more than the market narrative suggests.
The market is not replacing SIEM. It is rebuilding missing pieces.
They say they will reduce alert volume, improve detections, make investigations faster, lower storage costs, and simplify operations. None of that is new. Those were always core parts of the SIEM vision.
That is why so many of these new entrants exist. They found real gaps:
Pricing that became too hard to justify
Architectures that did not scale as well as they should
Detection stacks that still require too much manual work
Default content that creates too much noise
Workflows that remain painful for analysts and service providers
This is why I do not buy the “SIEM is over” narrative. If incumbents fix these gaps, many point solutions lose their edge quickly.
AI SOC is mostly a patch on downstream pain
The strongest short-term value in the AI SOC market is obvious: too many teams, especially MSSPs and down-market security providers, are drowning in alerts. A lot of environments are running with default content, light tuning, and limited budget for customization. Large enterprises can afford deep implementation and constant refinement. Many managed providers cannot.
If a product makes the SOC quieter without improving coverage, you may not have solved the problem. You may have just converted visible false positives into invisible false negatives.
If a startup is solving alert overload by learning that the same service-account misconfiguration fires every morning at 8am and can safely be deprioritized, that is useful. But it is still a patch on bad upstream logic, and it often hides a second problem: false negatives. Once teams see fewer alerts, they assume the system got smarter. Sometimes it did. Sometimes it just got quieter. The real fix belongs closer to the detection layer, the correlation logic, the content, and the configuration model.
That is why I think a lot of the current AI SOC wave is temporary in its present form. Not temporary because the need goes away, but temporary because the best parts of that value will be absorbed elsewhere. Some of it should move back into the SIEM. Some of it should live in the detection engine. Some of it belongs in better onboarding, better rule tuning, better data handling, and better defaults.
There is still room for new winners here. But “we reduce alerts by 80%” is not a durable thesis by itself.
The architecture debate is not centralized versus federated. It is about access patterns.
In theory, pushing compute to where the data sits is attractive. In practice, the answer depends on access patterns.
Some data absolutely does not need to be centralized all the time. Endpoint system calls are a good example. You do not want to shovel every low-level signal into a central platform by default if you can process, summarize, or prioritize it earlier.
But the moment an analyst, agent, or investigation workflow needs context, enrichment, and cross-correlation, some centralization comes back. You need to connect what happened on the endpoint with what happened on the firewall, identity plane, SaaS layer, email stack, and elsewhere.
So the future is probably not pure centralized or pure federated. It is hybrid:
Keep some data local or near-source
Route and centralize the parts that matter
Pull deeper context only when needed
Optimize around how investigations actually happen
This is why I keep coming back to smart data routing. Most organizations do not need to send every piece of data to the same place forever. But they do need an architecture that knows when to summarize, when to correlate, and when to pull more detail back in.
Data pipelines became the Trojan horse
Vendors in this space positioned themselves as optimization and routing tools. Send your data here, normalize it, trim low-value volume, route it to the right storage tier, keep costs down, and retain optionality. In many environments, that solved a real problem.
Once a pipeline vendor owns your ingestion layer and your integrations, it becomes an abstraction layer between you and the SIEM. That makes the SIEM less sticky. At first the pipeline vendor only routes data. Then it adds search. Then it runs lightweight detections. Then it supports simple rules. At some point it starts to look suspiciously like a simple SIEM.
If someone else owns the data path, they eventually get a shot at owning more of the security brain.
Pricing remains one of the category’s hardest unsolved problems
Almost everyone agrees that SIEM pricing has been a problem. Much fewer people agree on what the right answer is.
The vendor reality is straightforward: data volume drives cost. The customer reality is equally straightforward: they hate unpredictability.
That tension gets even worse in the service-provider world. MSSPs and MSPs often sell packaged services, per-user offerings, or per-device contracts. Their customers do not want a fluctuating bill because log volume spiked this month. So the thing that is economically clean for the vendor can be operationally ugly for the buyer.
There is no perfect answer here. But the next generation of pricing models will need to do a better job of separating:
Predictable commercial packaging
Actual backend resource consumption
Incentives for better data quality rather than more raw ingestion
The market has already started experimenting. Bring-your-own-storage, bring-your-own-compute, lower-cost data lakes, and more selective routing are all responses to the same pressure. Pricing is one of the core forces reshaping the market.
Detection engineering still needs much more help from the platform
Rules still need adaptation by environment. Thresholds differ. Data quality differs. Sources differ. Customer expectations differ. Generic content does not simply drop in and work.
What is surprising is how much low-hanging product work still remains. A modern platform should do far more to help users answer basic but critical questions:
Is the data required for this detection even present?
Is it configured in a way that can ever make this rule fire?
Are there obvious gaps or mistakes in the source configuration?
Which detections are silent because they are poorly mapped to the environment?
The more interesting direction, in my view, is not just better standalone rules. It is better context. Call it a context graph, an entity graph, a risk graph, or something else. The naming matters less than the function.
You want a living model of users, devices, applications, identities, behaviors, and risk signals. If the system knows that a user is coming from their normal IP, on a familiar device, through a known browser pattern, after strong authentication, that should shape how other events are interpreted. If all of those signals change at once, that should shape the response differently.
That kind of context is where detection quality meaningfully improves.
Network telemetry is not “back,” but it is still critical
I do not think this automatically means a major standalone NDR renaissance. But I do think many teams went too far in treating network telemetry as secondary once endpoint and application visibility improved.
An endpoint is still a single point of failure. If you lose visibility there, the network can still tell you a lot. It can help validate what else is happening. It can show you unmanaged systems, OT environments, choke points, and traffic patterns you will not otherwise see clearly.
This matters even more now because some organizations are reassessing where systems and data live. In parts of Europe, I am seeing more discussion around data sovereignty, political trust, private clouds, and selective moves back toward local or regional infrastructure. As architectures spread and governance constraints tighten, network visibility becomes more important again.
So no, I would not frame this as “throw away EDR and buy NDR.” That is the wrong lesson.
What happens next
The real question is not whether SIEM survives. It is which vendors understand they are now selling data architecture, detection quality, analyst workflow, and decision support.
The SIEM market is heading into another rebuild cycle. Some AI SOC and pipeline startups will disappear, some will be absorbed, and some incumbents will finally fix what they should have fixed years ago. But the core need is not going away: security teams still need a place where signals come together, context gets built, detections improve, and response decisions get made.
That is still SIEM territory, even if the implementation looks very different from what we used to buy.
? If you are building, buying, operating, or replacing SIEM, I’d love your input. I’m collecting market data at raffy.ch/SIEM. Anyone can contribute, and everyone is welcome.
And why most of the arguments do not hold up under scrutiny
Over the past 18 to 24 months, venture capital has flowed into a fresh wave of SIEM challengers including Vega (which raised $65M in seed and Series A at a ~$400M valuation), Perpetual Systems, RunReveal, Iceguard, Sekoia, Cybersift, Ziggiz, and Abstract Security, all pitching themselves as the next generation of security analytics. What unites them is not just funding but a shared narrative that incumbent SIEMs are fundamentally broken: too costly, too siloed, too hard to scale, and too ineffective in the face of modern data volumes and AI-driven threats.
This post does not belabor each startup’s product. Instead it abstracts the shared assertions that justify recent funding and then stresses them to see which hold up under scrutiny. I am not defending incumbents. I am trying to separate real gaps from marketing (and funding) narratives.
The “SIEM is Broken” Narrative
A commonly cited industry report claimed that major SIEM tools cover only about 19% of MITRE ATT&CK techniques despite having access to data that could cover ~87%. That statistic is technically interesting but also deeply misleading: ATT&CK technique coverage is not an operational measure of detection quality or effectiveness, it primarily reflects rule inventory and tuning effort. Nevertheless, it has become a core justification for the “SIEM is obsolete” narrative. I wasn’t able to find the original report to validate what and how they tested, but I have seen SIEMs that cover much more and have big detection teams taking care of these issues.
The Five Core Claims Driving the Market Thesis
Across decks, interviews, and marketing copy, I picked five recurring themes that define what these companies think incumbents get wrong and what investors are underwriting as the path forward.
1. “Centralized SIEM architectures no longer scale”
The claim is that forcing security telemetry into a centralized repository is too expensive and too slow for modern enterprises generating terabytes of logs every day. The proposed fixes include federated queries, analyzing data where it lives, and decoupling detection from ingestion so you never have to move or duplicate all your data.
The challenge is that correlation, state, timelines, and real-time detection require locality. Distributed query engines excel at ad-hoc exploration but are not substitutes for continuous detection pipelines. Federated queries introduce latency, inconsistent performance, and complexity every time you write a detection. Normalization deferred to query time pushes complexity into every rule. You do not eliminate cost, you shift it to unpredictable query execution and compute costs that spike precisely when incidents occur. Centralizing data isn’t a flaw; it is a tradeoff that supports correlation engines, summary indexes, entity timelines, and stateful detections that distributed query models struggle to maintain in real time. In fact, if the SIEM was to store the data in the customer’s S3 bucket, you can keep cost somewhat under control.
2. “SIEM pricing is broken because it charges by data volume”
A frequent refrain is that incumbent SIEMs penalize good security hygiene by tying pricing to ingestion volume, which becomes untenable as data grows. The proposed response is pricing models untethered from volume, open storage, and customer-controlled compute.
The challenge is that cost doesn’t vanish because you hide volume. Compute, memory, enrichment, retention, and query costs all remain. If pricing is detached from ingestion, it typically reappears as unpredictable query charges, usage tiers, or gated features. Volume is not an arbitrary metric; it correlates with the cost a vendor (or customer) incurs. Treating cost as orthogonal to data volume does not make it disappear; it just blinds you to a key cost driver. I have dealt with all the pricing models: by user, by device, by volume, … in the end I needed to make my gross margins work, guess who pays for that?
3. “SIEM detections are weak because they rely on bad rules”
New entrants commonly assert that traditional SIEM rules are noisy, static, and unable to keep up with modern threat techniques. Solutions offered include natural-language detections, detections-as-code, continuous evaluation, and AI-generated rules.
The challenge is that many of these still sit atop the same primitives. For example, SIGMA is widely used as a community detection language, but it is fundamentally limited: it is mostly single-event, cannot express event ordering or causality, has no native temporal abstractions or entity-centric modeling, and cannot natively express thresholds, rates, cardinality, or statistical baselines. Wrapping these limitations in AI or “natural language” does not change the underlying detection physics. You can improve workflow and authoring experience, but you do not fundamentally invent a new class of detection with the same primitives. And guess what, large vendors have pretty significant content teams – I mean detection engineering teams – often tied into their threat research labs. Don’t tell me that a startup has found a more cost effective and higher efficacy way to release detection rules. If that were the case, all these large vendors would be dumb to operate such large teams.
4. “SIEMs lack context, causing false positives”
The argument here is that existing SIEMs flood analysts with alert noise because they lack deep asset context, threat intelligence, or behavioral understanding. New entrants promise tightly integrated TI feeds, cloud context, or built-in behavior analytics.
Context integration has been a focus of incumbent platforms for years. The real hard problem is not accessing context but operationalizing it without drowning analysts. More feeds often mean more noise unless you have mature enrichment pipelines, entity resolution, and risk scoring built into rules that understand multi-stage attack sequences. Adding more sources does not automatically improve signal quality. The noise problem is as much about rule quality and use-case focus as it is about context availability. Apply the same argument here with regards to the quality of threat feeds that I outlined in the last item.
5. “AI-native SIEMs will finally fix detection and response”
Perhaps the most seductive claim is that incumbent SIEMs were built for a pre-AI world and that new platforms built with agentic AI at every layer will finally crack automation, detection, and investigation.
The challenge is that AI does not eliminate the need for structured, high-quality, normalized data, or explainability, or deterministic behavior in high-risk contexts. AI can accelerate workflows, assist with investigation, and suggest hypotheses, but it does not replace the need for precise, reproducible, and auditable detection logic. Most AI-native claims today are improvements in UX and speed, not architectural breakthroughs in detection theory.
The Uncomfortable Conclusion
VC money is flowing because SIEM is operationally hard, expensive, and often unpopular with SOC teams. There is real pain and real gaps, especially around cost transparency, scaling, and usability. But declaring existing SIEMs obsolete because they are imperfect is not a thesis; it is a marketing slogan.
The core assumptions driving this funding wave deserve scrutiny: centralization is treated as a flaw rather than a tradeoff necessary for continuous detection, pricing complaints get conflated with architectural insights, detection quality is blamed on tooling rather than operational realities, and AI is overstated as a panacea.
On the flip side, here are a couple of directions that should be looked at:
Some of the new entrant SIEMs actually make a dent. They are rebuilding their entire pipelines and storage architecture with modern technologies, not old paradigms. They have a clear advantage and don’t have to deal with millions of lines of tech debt. Using an agentic AI architecture could be quite interesting here.
As the AI SOC emerges – and maybe become a reality – we will probably see more and more MCP servers exposing infrastructure information that can be leveraged, from alerts to context to response capabilities. But we’ll need to see how data schemas and all that will evolve.
The one innovation that has already generated some returns for investors is the entire data pipeline world. Companies like Observo (I had the privilege to be an advisor) have truly added something useful to the SIEMs and as I argue in one of my previous blogs, needs to really become a capability baked into each SIEM out there.
