Overcoming tool fragmentation and data gaps in the AI-powered SOC

Most organizations operate in highly fragmented security environments, with 88% using 10 or more tools for detection, investigation, and response. This fragmentation, coupled with the slow onboarding of new data sources, which can take weeks or months, delays detection investigations and complicates responses. Additionally, there's a lack of visibility across the full security data estate, leaving important telemetry underused. 

It's time to move away from legacy methods toward unified detection fabrics that integrate behavioral analytics, real-time correlation and adaptive AI-powered workflows to break down silos and accelerate threat detection.

Challenges of fragmented tooling

When there are gaps in visibility, it's not necessarily because there are tools missing; it's often because there are too many tools. Identity and cloud are still the least monitored areas of the security operations center (SOC). Yet, even when telemetry is available, tooling fragmentation continues to undermine effective detection and response.

In the 2025 Pulse of the AI SOC Report, just 12% of participants noted the use of fewer than 10 tools for threat detection, investigation and response. Most respondents reported working within environments that are significantly more complex: 38% use 10 to 19 tools, 29% use 20 to 29 tools, and 16% use 30 or more tools. Such fragmentation is hard to integrate, but there's more: it brings operational friction at almost every stage of the incident lifecycle. Analysts are overwhelmed as identity-based threats rise and tool fragmentation slows response.

Slow onboarding and lack of visibility

Operational trade-offs, rather than technology limitations, are typically the cause of telemetry gaps. Security team leads must often make hard choices regarding which data sources they can reasonably afford to ingest. Teams even stall the onboarding of high-value telemetry in some cases as they wait for a vendor to support a new integration or build a parser. In other cases, budget makes the decision, as bringing certain data into the SIEM is too expensive.

Consequently, key signals – particularly from SaaS, cloud and identity systems – might be overlooked or underused, not because they aren't available but because they weren't prioritized at the point of integration. SOCs require more than just broader data access; they need platforms that can unite, correlate, and contextualize behavior, privilege, and identity in real-time across a fragmented ecosystem.

One of the long-standing and most dangerous vulnerabilities of the modern SOC is its visibility gaps. It's astonishing that a mere 4% of survey participants report having total visibility across their security data estate. The other 96% note major blind spots, most often in cloud infrastructure (74%) and identity and access behavior (67%).

These gaps constitute more than operational irritants. They align directly with three of the top four threat concerns teams have: cloud-specific risks resulting from vulnerabilities and misconfigurations, phishing and social engineering (including AI-enhanced lures), and multifactor authentication (MFA) bypass and account takeover. Endpoint telemetry, SaaS application activity, and encrypted east-west traffic also reveal the ways visibility becomes incomplete and fragmented as infrastructure decentralizes.

Fixing the problem

Data pipeline management (DPM) can help address the data gaps/visibility problem. DPM helps by optimizing the data feeding to the SIEM and clearing out the noise and budget to make room for missing critical sources. However, that solves only part of the problem.

With a tsunami of alerts and overwhelmed security teams, many SOCs have turned to AI for help. AI isn't meant to replace analysts but to assist them and provide the capability to stay ahead of threats. Manual triage, detection rules, signatures, and other standard methods can't match either the pace of modern operational speed and scale or the increasing number of attacks. AI offers more than productivity gains; it offers transformation via alert triage, context gathering, correlation of behavioral signals and analyst guidance with explainable recommendations for remediation. 

Many teams have already sought the help of AI; 31% of respondents are using AI across multiple SOC workflows, from detection and triage to enrichment and response. A further 34% run targeted pilots, while 22% use AI to evaluate use cases. All told, 875 of the responding companies are shifting to AI adoption and integration within the SOC. Rather than an experiment, this shift is strategic with respect to how the SOC functions.

AI is no longer an emerging or theoretical technology; it's a practical, operational solution to significant, deep-seated problems. Security leaders have stopped asking if the SOC should use AI and are now asking how to deploy it securely and effectively.

Farewell, Fragmentation

Rather than trying to automate everything all at once, the SOC must know what to automate first. AI integration must be incremental, starting with repetitive Tier 1 analyst work to build trust in the early gains and then scaling gradually. This means context, visibility, and human judgment are baked in from the beginning. AI is becoming the modern SOC's operating system, offering measurable gains in clarity, speed and scalability.

AI has already begun to reduce investigation time, take over repetitive triage, and afford analysts some breathing room so they can think more strategically. However, translating early wins into long-term impact involves moving beyond hype and pilots to workflows that actually deliver. SOC leaders must navigate the process of incorporating AI everywhere it should be, which will lead to clarity, transparency, and results that speak for themselves.