AI-Powered Security: How ‘Bounded Autonomy’ Can Rescue Overwhelmed SOC Teams
As alert volumes surge and burnout threatens cybersecurity professionals, a new approach—leveraging AI with human oversight—is emerging as a critical path forward for Security Operations Centers.
The average enterprise Security Operations Center (SOC) is inundated with 10,000 alerts per day. Each alert demands 20 to 40 minutes for proper investigation, a workload that even fully staffed teams can only address 22% of the time. Alarmingly, over 60% of security teams admit to ignoring alerts that later proved to be critical, highlighting a growing crisis in threat detection and response. The relentless pressure is driving a wave of burnout, with senior analysts even contemplating career changes.
The traditional SOC model is demonstrably unsustainable. Legacy systems often deliver conflicting alerts and struggle to integrate, creating a chaotic environment ripe for errors and exhaustion. But the nature of the work itself is evolving, with routine tasks increasingly automated and a new emphasis on leveraging artificial intelligence.
The Rise of Agentic AI and Bounded Autonomy
Tier-1 analyst responsibilities—triage, enrichment, and escalation—are rapidly becoming software functions. More SOC teams are turning to supervised AI agents to manage the sheer volume of alerts, freeing human analysts to focus on complex investigations, reviewing AI decisions, and handling edge-case scenarios. This shift is accelerating response times, but it’s not without risk.
Gartner predicts that over 40% of “agentic AI” projects will be canceled by the end of 2027, primarily due to a lack of clear business value and inadequate governance. Successfully integrating AI requires careful change management and preventing generative AI from becoming a source of instability within the SOC.
The key to success lies in what’s being called “bounded autonomy.” This approach utilizes AI agents to automatically handle triage and enrichment, but reserves final containment actions for human approval when the potential severity is high. This division of labor allows organizations to process alert volume at machine speed while retaining human judgment for critical decisions.
Seeing the Network Differently with Graph-Based Detection
Traditional Security Information and Event Management (SIEM) systems often present isolated events, making it difficult to understand the broader context of an attack. Graph-based detection is changing this paradigm. By visualizing the relationships between events, AI agents can trace attack paths more effectively, rather than triaging alerts individually. For example, a suspicious login appears far more concerning when the system recognizes the account is only two connections away from the domain controller.
The benefits are measurable. AI is demonstrably compressing threat investigation timeframes and increasing accuracy when compared to senior analyst decisions. Deployments have shown AI-driven triage achieving over 98% agreement with human experts, while simultaneously reducing manual workloads by more than 40 hours per week. However, as one analyst noted, “Speed means nothing if accuracy drops.”
ServiceNow and Ivanti Lead the Charge to Agentic IT Operations
The shift towards AI-powered security isn’t limited to dedicated SOC solutions. Gartner forecasts that multi-agent AI in threat detection will surge from 5% to 70% of implementations by 2028. ServiceNow, having invested approximately $12 billion in security acquisitions in 2025 alone, is at the forefront of this trend. Ivanti, after accelerating its kernel-hardening roadmap in response to nation-state attacks, has also announced agentic AI capabilities for IT service management, extending the bounded-autonomy model to the service desk. A customer preview is scheduled for Q1 2026, with general availability following later in the year.
The challenges facing SOCs are increasingly impacting service desks as well. Robert Hanson, CIO at Grand Bank, explained, “We can deliver 24/7 support while freeing our service desk to focus on complex challenges.” This ability to provide continuous coverage without a proportional increase in headcount is driving adoption across financial services, healthcare, and government.
Establishing Governance Boundaries for AI Autonomy
Implementing bounded autonomy requires explicit governance boundaries. Teams must clearly define which alert categories agents can address autonomously, which require mandatory human review regardless of confidence scores, and the appropriate escalation paths when certainty falls below a defined threshold. High-severity incidents should always require human approval before containment actions are taken.
Having robust governance in place before deploying AI across SOCs is crucial to realizing the time and containment benefits these tools offer. As adversaries increasingly weaponize AI and exploit vulnerabilities faster than defenders can respond, autonomous detection is becoming essential for maintaining resilience in a zero-trust world.
A Path Forward for Security Leaders
Security leaders should prioritize automating workflows where failure is recoverable. Three areas offer immediate opportunities: phishing triage (missed escalations can be caught in secondary review), password reset automation (low blast radius), and known-bad indicator matching (deterministic logic). These three workflows consume 60% of analyst time while contributing minimal investigative value. Automating these tasks first, and then validating accuracy against human decisions for a 30-day period, provides a low-risk entry point for AI integration.
As Matthew Sharp, CISO at Xactly, succinctly put it: “Adversaries are already using AI to attack at machine speed. Organizations can’t defend against AI-driven attacks with human-speed responses.” The future of cybersecurity hinges on embracing AI, but doing so responsibly, with a clear understanding of its limitations and the critical need for ongoing human oversight.
