Srajan Gupta, Security Engineering @ Dave • June 23, 2025
Introduction
Security operations succeed or fail on one outcome: reducing risk. Measuring or quantifying risk is often challenging and no one KPI is a perfect representation of risk. However, two metrics are often used: mean-time-to-detect (MTTD) and mean-time-to-respond (MTTR).
When a SOC compresses MTTD and MTTR, three things happen immediately:
Exposure windows collapse. Adversaries have less time to execute additional steps in the attacker’s kill chain, exfiltrate data, or ransom systems.
Breach costs plummet. IBM’s 2024 Cost of a Data Breach shows that organizations with extensive security-AI/automation saved US $2.22 million per incident, shaving the global average breach cost from $4.88 million to $2.66 million.
Board-level risk drops to the balance sheet. The same report notes the lifecycle of a breach still averages 258 days from infiltration to containment. Every day that number falls is a quantifiable reduction in legal exposure, regulatory fines and customer churn.
The inefficiency is measurable: 84 % of organizations say their analysts unknowingly investigate the same incident several times a month because current tools, like SOARs, fail to correlate related alerts. And the 2024 SANS SOC Survey lists “lack of automation and orchestration” as the single most-cited barrier to effective operations.
As such, we’ve seen the emergence of “AI SOC” companies, which are products that leverage a combination of deterministic and non-deterministic decision making to autonomously investigate alerts. So any “AI SOC” worth its license key must prove it can move those numbers, not just color dashboards green. Risk is not abstract in this case - it’s a direct reflection of how long an attacker is undetected and not contained in your environment.
What First-Wave “AI SOC” Tools Actually Do
The first commercial wave of AI-for-SOC products promised an “autonomous Tier-1 analyst.” In practice, most tools bolt a large-language model onto an existing SIEM or SOAR stack:
Handy for noise reduction but four structural gaps keep risk stubbornly high:
Gartner’s 2024 note, bluntly titled “There Will Never Be an Autonomous SOC", captures the frustration: until AI spans far more than Tier-1 triage, a self-driving SOC is fantasy.
Security Operations ≠ Alert Triage - It’s a Risk-Reduction Assembly Line
Risk reduction is a combination of people, processes, and technology. High false positive rates might lead to more detections but reduce overall effectiveness as people lose trust in the alerting system. But then, overturning may result in high false negative rates and blind spots. So what would an ideal system look like that maintains human trust in the system without leading to blind spots?
A healthy SOC works like an engineering value stream, where each stage directly tamps down risk:
Break any link in the chain and residual risk simply hops stages instead of shrinking. Yet most first-wave AI products touch only the Investigation row. Intel ingestion, rule creation, tuning and hunting remain slow, manual and siloed-stretching that 258-day breach lifecycle like taffy.
A Practitioner’s Vision for Agentic AI in the SOC
To make a deniable dent in risk KPIs, AI must operate across the assembly line, collaborating the way a seasoned team does. Four architectural pillars emerge from real-world pilots and red-team exercises:
Data-first foundation: A security data mesh that links users, hosts, apps and cloud assets across every telemetry source-SIEM, EDR, cloud audit, identity, network. Context gaps are auto-filled; where entities are first-class citizens, instead of copy-pasting host or usernames across different platforms
Swarm of specialized agents: Think of an AI threat-intel analyst, an AI detection engineer, an AI Tier-2 investigator, an AI playbook drafter-each with its own skill set and memory. Agents exchange context through the mesh, just like humans chatting in Slack. This is where most likely where most efforts in SOC are currently put.
Goal-driven reasoning, not static playbooks: Agents break a problem into sub-tasks: “collect CloudTrail > correlate to device > check identity baseline > pivot to Flow Logs,” adjusting the plan as evidence lands. That adaptability is crucial for novel attack paths.
Continuous learning loop: Analyst feedback on false positives, post-incident lessons-learned, MITRE ATT&CK coverage gaps-all flow back to update detection rules and agent heuristics automatically. The SOC improves every day, not every quarterly tuning sprint.
When this loop hums, you see Intel → Detection latency drop from weeks to minutes, duplicated and incomplete investigations vanish, and human analysts review decisions instead of gathering raw logs.
Human + AI Collaboration - the Hinge on Which This Vision Turns
Several vendors are racing toward this agentic blueprint; Kenzo Security’s platform offers a live example of how it can work.:
Role-based agents atop a shared data mesh. Kenzo’s Security Data Mesh unifies telemetry from 150 + tools into an entity-centric graph. A threat-intel agent consumes unstructured blogs/feeds, a detection agent generates or tunes rules, and a Tier-2 agent autonomously investigates 100 % of alerts with no volume limits.
Explainable decision making. Each autonomous closure bundles the step-by-step pivots, log excerpts and risk reasoning so analysts audit in minutes, not hours.
Feedback-driven tuning. When an analyst stamps an incident “benign,” a detection agent proposes suppression logic; analyst approval feeds the next learning cycle-closing the loop that most SOAR scripts ignore.
Risk-centric surfacing. Rather than dump 1,000 high-severity alerts, Kenzo clusters related signals and bubbles up high-risk entities-users, hosts, SaaS tenants, kubernetes clusters-putting human eyes where they matter most.
Detection Recommendations. Kenzo’s detection agent continuously analyzes your environment’s activity baseline and alert history to propose high-signal detection logic and tuning recommendations. Each recommendation includes rationale, supporting evidence, and expected impact so analysts can approve, adapt, or reject with confidence.
The qualitative result is that analysts spend time making decisions, not gathering information. The quantitative result is a reported 10× reduction in MTTR for customers and increased rate of detection coverage expansion.
Critically, Kenzo does not claim to replace humans. It treats agents as teammates who are coachable.
Checklist for evaluating an AI SOC Today
Use these questions to separate hype from risk-reducing reality:
If a platform can’t provide evidence-case studies, before/after metrics-walk away.
Conclusion: The Road from Buzz to Risk Delta
Security-AI hype is everywhere, yet the average breach still lingers 258 days. Vendors selling “autonomous SOCs” that only summarise alerts are tinkering at the edges of risk, not transforming it.
True Agentic SOC solutions must:
Have a data first approach.
Think in entities and campaigns, not isolated alerts.
Collaborate with humans through transparent, auditable reasoning.
Treat risk reduction (falling MTTD/MTTR and breach cost) as the sole success metric.
Be multifunctional.
The vendors that understand this, like Kenzo, aren’t chasing buzzwords. They’re building tools that slot into your team, not tools that pretend to be your team. Demand platforms that prove-with numbers-they compress breach lifecycles and slash duplicate toil. Until they do, AI in the SOC is just another blinking light.
Remember: less dwell time equals less risk. Choose AI that demonstrably moves that needle, and the board will thank you-not for adopting the latest buzzword, but for materially fortifying the business.
If you’re ready to level up your security operations, schedule a demo with Kenzo Security today.
