Security operations centers are at a breaking point. Alert volumes are climbing, analyst headcount is not, and the tools meant to help are often making it worse. In a recent Cyber Security Tribe video roundtable cybersecurity and technology leaders discussed how security operations centers are under pressure to do more with less, while the complexity of modern environments continues to grow. Against this backdrop, artificial intelligence is being explored not as a futuristic concept, but as a practical tool to address long-standing operational challenges. The conversation addressed both optimism and caution, with participants outlining clear expectations for what an AI-driven SOC solution must deliver to create real value.
The Breaking Point of Alert Overload and Limited Clarity
One of the issues raised was the overwhelming volume of alerts and the difficulty of distinguishing meaningful signals from background noise. Many organizations have invested heavily in detection tools over the past decade, yet analysts still face a daily flood of notifications that demand attention. As one participant noted, “We are not lacking data. We are drowning in it.” Industry research suggests the average enterprise SOC processes over 11,000 alerts per day, and analyst turnover continues to climb. This imbalance has led to fatigue. This imbalance has led to fatigue among analysts, slower response times, and increased risk that critical threats may be missed.
The group emphasized that any AI solution entering this environment must prioritize reduction of noise rather than contributing to it, and there was a strong consensus that automation should focus on triage and prioritization. Instead of simply generating more alerts or insights, AI must help teams identify what truly matters. “If it cannot help my analysts focus on the top one percent of issues, it is just another tool,” one attendee said. This sentiment reflects a broader frustration with solutions that promise intelligence but ultimately add complexity.
Why Seamless Integration Matters More Than New Features
Closely tied to the issue of alert fatigue is the reality of how security teams operate day to day. SOC environments are built on layered tooling and established workflows that span SIEM platforms, endpoint tools, and case management systems. Within this ecosystem, participants expressed skepticism toward standalone AI solutions that require separate interfaces or parallel processes.
“We do not need another dashboard,” one participant said. “We need something that works where our analysts already are.” This highlights a key requirement for vendors. Integration is not just a technical feature, but a critical factor in adoption. Solutions that disrupt workflows or demand significant retraining are unlikely to gain traction, regardless of their capabilities.
The need for correlation across disparate data sources was also raised. Modern organizations generate security-relevant data from a wide range of environments, including cloud infrastructure, enterprise applications, and connected devices. In particular, gaps in visibility remain a concern in areas such as IoT and product ecosystems, where telemetry often does not align cleanly with centralized SOC processes.
Participants expressed interest in AI’s ability to bridge these gaps by correlating signals across domains and providing a unified view of potential threats. However, this capability must be paired with accuracy. False positives remain a significant concern, especially in environments where incorrect actions could disrupt critical systems. As one participant cautioned, “If the system is wrong too often, people will stop trusting it. And once trust is gone, it is very hard to get back.”
Building Trust Through Transparency and Controlled Automation
Trust and explainability were recurring topics throughout the conversation. While there is enthusiasm for automation, there is also a clear need for transparency in how AI systems arrive at their conclusions. Security teams are accountable for their actions, and decisions made by automated systems must be understandable and defensible.
“I need to be able to explain why something was flagged or why an action was taken,” one attendee explained. “It cannot be a black box.” This expectation is especially critical in regulated industries, where auditability is not optional. Participants highlighted the importance of traceability, where every decision can be linked back to data and logic that can be reviewed and validated.
Beyond detection and analysis, the discussion turned to the role of AI in decision support. Many current tools excel at identifying potential issues, but they fall short when it comes to guiding responses. Analysts are often left to interpret findings and determine the appropriate course of action, which can be time-consuming and inconsistent.
AI has the potential to close this gap by offering recommendations or even executing predefined responses. Still, participants drew a clear distinction between recommendation and action. While there is interest in systems that can automate response, there is also a desire for safeguards. “Automation is powerful, but it needs guardrails,” one participant noted. “You cannot have something making changes in your environment without clear controls.”
Demonstrating Real Impact in a Resource-Constrained SOC
In an environment where budgets are closely scrutinized, security leaders must demonstrate the value of their investments. AI solutions are expected to deliver tangible improvements, such as reduced mean time to detect and respond, increased analyst productivity, and lower operational costs. “We need to see real metrics,” one attendee said. “If it cannot show impact, it is hard to justify.”
This focus on outcomes extends to the broader issue of tool sprawl, and the average enterprise SOC runs 45 or more security tools. Most generate alerts. Few talk to each other. This fragmentation makes environments difficult to manage. Participants expressed a desire for consolidation and simplification, with AI potentially serving as a unifying layer. Instead of adding another point solution, the ideal offering would enhance and connect existing tools, reducing complexity rather than increasing it.
Resource constraints were another important factor discussed. Security teams are often understaffed, and finding skilled talent remains a challenge. In this context, AI is seen as a way to augment human capabilities and extend the reach of existing teams. “We cannot hire our way out of this problem,” one participant observed. “We need technology that helps our people do more.”
Ease of use and speed of deployment were highlighted as critical considerations for organizations with limited resources. Solutions that require extensive configuration or specialized expertise may be difficult to implement. There is a strong preference for tools that can deliver value quickly and operate with minimal overhead. “If it takes six months to get up and running, it is probably not going to work for us,” one attendee remarked.
Scalability also remains a key concern, and as organizations grow and their environments become more complex, their security operations must scale accordingly. Participants expressed interest in AI solutions that can handle increasing volumes of data and activity without a corresponding increase in manual effort.
“There is a gap between detection and action,” one CISO explained. “We need something that can carry us through the entire process.” This end-to-end perspective reflects a desire for solutions that go beyond isolated capabilities and address the full lifecycle of security operations.
At the same time, there was recognition that AI is not a cure-all. Attendees emphasized the importance of setting realistic expectations and aligning technology with actual operational needs. “This is not about replacing people,” one cybersecurity leader said. “It is about making them more effective.”
Finally, the discussion explored the growing need to connect security operations with business outcomes. Security leaders are increasingly expected to communicate risk in terms that resonate with executive stakeholders. This requires translating technical findings into business contexts, such as financial impact or operational disruption.
“We need to move from technical alerts to business relevance,” one participant noted. “That is how you get attention at the leadership level.” This perspective underscores the strategic importance of security operations and highlights another area where AI can provide meaningful value.
Taken together, these insights paint a clear picture of what organizations expect from AI in the SOC. They are not looking for novelty; they are looking for solutions that reduce friction, enhance decision-making, and deliver measurable results. Vendors that can meet these expectations will be well positioned to play a meaningful role in the next evolution of cybersecurity operations. The message from the room was unambiguous: the next generation of SOC tooling will not win on features. It will win on friction - specifically, how much of it the tool removes from the analyst’s day.