Security Information and Event Management (SIEM) systems have, for the past two decades, helped security teams analyze activity across their IT systems to identify potential threats. They were originally designed to solve the problem of dispersed logs, giving teams their first reliable and centralized way to see what was happening across their network. As such, they were a transformative step in how organizations managed security data and demonstrated that visibility could be turned into actionable insight.
More recently, however, the limitations of SIEMs have become increasingly apparent and concerning. Instead of improving security standards, as initially intended, many organizations are faced with spiralling SIEM costs, low-value security outcomes, and excessive alert volumes that distract security teams from addressing other important priorities.
SIEMs transitioned from being the SOC standard to a legacy cost-center whose model is collapsing.
Take pricing, for example. Traditional SIEM pricing is typically tied to the volume of ingested data that needs to be analyzed. While this model incentivises vendors, it also penalises customers, with security leaders often facing the dilemma of whether to prioritize visibility or the budgets required for other security solutions. For those organizations under budgetary pressure – and there are many – reducing ingestion to control costs has become inevitable. Indeed, industry research suggests that 65% of security leaders have already limited log intake due to expense, reducing their ability to detect threats even while continuing to spend heavily. From a security and compliance perspective, this tradeoff simply isn’t sustainable.
Then there’s the analyst toll of tuning and maintaining correlation rules—the predefined logic that links signals and alerts from different threat detection systems to potential security threats. Once seen as a valuable innovation, these rules degrade rapidly. Threat actor tactics change daily, while rule libraries are often updated only weeks or months later, creating gaps and unnecessary noise.
In this context, SIEM alerts frequently reflect legitimate behaviour rather than genuine threats. A common example today is MFA push fatigue. Many SIEMs flag multiple MFA prompts as suspicious, but in reality, employees often trigger them by mistake, or apps send repeated requests due to misconfigurations. What was intended as a signal of compromise has become yet another stream of false positives that drains analyst time.
Widely known as ‘alert fatigue’, this issue has become an industry-wide bugbear. According to one study, a quarter of security analysts’ time is now spent dealing with false positives. In fact, the problem can actually increase security risk, with research from Verizon revealing that 74% of breaches generated alerts that were ignored, largely because analysts were overwhelmed by volume.
Looking to the future
Despite these difficulties, SIEM-generated security alerts remain central to the approach many organizations take to threat detection. Even when legitimate threats are identified, however, security teams still have a significant amount of triage and investigative work to do to contain risks and, where necessary, take drastic action to mitigate a breach.
In these situations, SIEMs will flag security threats, but they don’t help teams connect the dots around why something has happened or what to do next. This has contributed to an environment where effective security depends on integrating a multitude of tools (more than 20 per organization on average, according to research) to complete an investigation. Instead of a centralized and streamlined workflow, teams are forced into time-consuming pivots, much like the ‘swivel chair effect’ to gather enough context to decide the alert’s maliciousness.
So what needs to change? Thankfully, the building blocks for modern security operations are already in place. Specifically, cloud-native technologies for ingesting, archiving, and querying security data are now widely accessible and are no longer the proprietary domain of SIEM vendors. That means SOCs no longer have to accept traditional platforms’ constraints, costs, and lock-in.
The most advanced systems now make it possible to ingest and store logs at scale without the financial penalties of older platforms. Instead of being locked into expensive vendor vaults, organisations can use low-cost, flexible cloud archives that deliver both scale and data ownership at significantly lower cost. Some vendors go further by allowing teams to run these services on their own infrastructure, unlocking even greater savings and control.
In common with just about every other sector, AI is also playing an increasing role. In particular, agentic AI systems are taking on the role of first-line analyst by filtering false positives, enriching context, and sharing only genuine threats. In doing so, they remove the need for complex playbook engineering and reduce the noise that has long overwhelmed SOC teams.
Once a threat is confirmed, integrated workflows and automation ensure that response can be completed in minutes rather than hours. This shortens dwell time and enables security teams to operate in a far more proactive way. The cumulative effect of these capabilities is significant, with analysts freed to spend more time investigating incidents that genuinely matter. Crucially, security teams no longer have to compromise due to budget constraints. The combination of AI-powered analysis and low-cost cloud archive storage can reduce logging expenses by up to 80%, enabling teams to redirect resources toward strategic initiatives and advanced technologies that are built to counter tomorrow’s threats.
The message is clear: SIEMs won’t vanish overnight, but their role as the centerpiece of security operations is already over. The next era will be defined by cloud-native data architectures, AI-driven triage, and streamlined response workflows that cut through noise instead of multiplying it. Security teams don’t need to accept high costs and low value as the status quo anymore. A more scalable, outcome-driven model is here—and it’s reshaping the future of the SOC.
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