The average enterprise SOC receives 10,000 alerts per day. Each requires 20 to 40 minutes to investigate properly, but even fully staffed teams can only handle 22% of them. More than 60% of security teams have admitted to ignoring alerts that later proved critical.
Running an efficient SOC has never been harder, and now the work itself is changing. Tier-1 analyst tasks — like triage, enrichment, and escalation — are becoming software functions, and more SOC teams are turning to supervised AI agents to handle the volume. Human analysts are shifting their priorities to investigate,...
The average enterprise SOC receives 10,000 alerts per day. Each requires 20 to 40 minutes to investigate properly, but even fully staffed teams can only handle 22% of them. More than 60% of security teams have admitted to ignoring alerts that later proved critical.
Running an efficient SOC has never been harder, and now the work itself is changing. Tier-1 analyst tasks — like triage, enrichment, and escalation — are becoming software functions, and more SOC teams are turning to supervised AI agents to handle the volume. Human analysts are shifting their priorities to investigate, review, and make edge-case decisions. Response times are being reduced.
Not integrating human insight and intuition comes with a high cost, however. Gartner predicts over 40% of agentic AI projects will be canceled by the end of 2027, with the main drivers being unclear business value and inadequate governance. Getting change management right and making sure generative AI doesn’t become a chaos agent in the SOC are even more important.
Why the legacy SOC model needs to change
Burnout is so severe in many SOCs today that senior analysts are considering career changes. Legacy SOCs that have multiple systems that deliver conflicting alerts, and the many systems that can’t talk to each other at all, are making the job a recipe for burnout, and the talent pipeline cannot refill faster than burnout empties it.
CrowdStrike's 2025 Global Threat Report documents breakout times as fast as 51 seconds and found 79% of intrusions are now malware-free. Attackers rely on identity abuse, credential theft, and living-off-the-land techniques instead. Manual triage built for hourly response cycles cannot compete.
As Matthew Sharp, CISO at Xactly, told CSO Online: "Adversaries are already using AI to attack at machine speed. Organizations can't defend against AI-driven attacks with human-speed responses."
How bounded autonomy compresses response times
SOC deployments that compress response times share a common pattern: bounded autonomy. AI agents handle triage and enrichment automatically, but humans approve containment actions when severity is high. This division of labor processes alert volume at machine speed while keeping human judgment on decisions that carry operational risk.
Graph-based detection changes how defenders see the network. Traditional SIEMs show isolated events. Graph databases show relationships between those events, letting AI agents trace attack paths instead of triaging alerts one at a time. A suspicious login looks different when the system understands that the account is two hops from the domain controller.
Speed gains are measurable. AI compresses threat investigation timeframes while increasing accuracy against senior analyst decisions. Separate deployments show AI-driven triage achieving over 98% agreement with human expert decisions while cutting manual workloads by more than 40 hours per week. Speed means nothing if accuracy drops.
ServiceNow and Ivanti signal broader shift to agentic IT operations
Gartner predicts that multi-agent AI in threat detection will rise from 5% to 70% of implementations by 2028. ServiceNow spent approximately $12 billion on security acquisitions in 2025 alone. Ivanti, which compressed a three-year kernel-hardening roadmap into 18 months when nation-state attackers validated the urgency, announced agentic AI capabilities for IT service management, bringing the bounded-autonomy model reshaping SOCs to the service desk. Customer preview launches in Q1, with general availability later in 2026.
The workloads breaking SOCs are breaking service desks, too. Robert Hanson, CIO at Grand Bank, faced the same constraint security leaders know well. "We can deliver 24/7 support while freeing our service desk to focus on complex challenges," Hanson said. Continuous coverage without proportional headcount. That outcome is driving adoption across financial services, healthcare, and government.
Three governance boundaries for bounded autonomy
Bounded autonomy requires explicit governance boundaries. Teams should specify three things: which alert categories agents can act on autonomously, which require human review regardless of confidence score, and which escalation paths apply when certainty falls below threshold. High-severity incidents require human approval before containment.
Having governance in place before deploying AI across SOCs is critical if any organization is going to get the time and containment benefits this latest generation of tools has to offer. When adversaries weaponize AI and actively mine CVE vulnerabilities faster than defenders respond, autonomous detection becomes the new table stakes for staying resilient in a zero-trust world.
The path forward for security leaders
Teams should start with workflows where failure is recoverable. Three workflows consume 60% of analyst time while contributing minimal investigative value: phishing triage (missed escalations can be caught in secondary review), password reset automation (low blast radius), and known-bad indicator matching (deterministic logic).
Automate these first, then validate accuracy against human decisions for 30 days.