Reducing the cost to triage an Incident

Security leaders are under pressure to do more with less: more alerts, more regulations, more cloud complexity—and not enough time or talent. So the real question isn’t whether AI is “nice to have.” It’s whether AI-driven workflows can produce a measurable return on investment (ROI) compared to a traditional, analyst-only SOC model.

This article breaks down the ROI debate using operational metrics that matter—time-to-resolution (TTR), cost-per-incident, and accuracy—and shows where AI-driven automation creates an edge, especially at scale.

Why ROI is harder than ever for traditional SOC models

Traditional SOC performance depends heavily on human expertise: analysts triage alerts, write queries (often KQL in Microsoft Sentinel), pivot across tools, and document actions in ticketing systems. This works—until volume spikes.

When threat volume rises or staffing dips, the system becomes fragile:

• Investigation time expands because every step is manual or semi-manual.
• Consistency drops modellimaintained, the same questions repeat during escalations, audits, or post-incident reviewsng becomes easier when you convert time into cost and account foras different analysts make different calls under time pressure. The question is whether AI-driven workflows can deliver
• Costs rise because high-skill time is spent on repetitive tasks (data gathering, enrichment, and documentation).

In ROI terms, traditional SOCs often “look fine” during normal conditions and then become disproportionately expensive during incident surges—exactly when business risk is highest.

The two ROI metrics that reveal everything: TTR and cost per incident

If you measure only one thing, measure TTR. If you measure two, add the cost per incident.

Time-to-resolution (TTR)

TTR includes detection, triage, investigation, containment guidance, and closure actions (including ticket updates and reporting). In many SOCs, the hidden time sink is not the decision—it’s the steps around the decision: collecting evidence, correlating signals, and documenting outcomes.

AI-driven workflows shorten TTR by automating the “busy work” and guiding the decision path.

Cost per incident

Cost per incident is typically driven by:

1. Analyst hours consumed (triage + investigation + reporting)
2. Escalation overhead (handoffs, delays, rework)
3. Tooling fragmentation (context switching)
4. Business impact duration (downtime, exposure window, stakeholder time)

AI reduces cost per incident by compressing time, preventing rework, and standardising outcomes through guided workflows.

Traditional analysts vs AI-driven workflows: What changes in practice?

The difference isn’t “humans vs AI.” The difference is human-led manual steps vs human-supervised automated workflows.

Traditional approach: Expertise bottlenecks

A skilled analyst is often forced into tasks like:

• Writing and tweaking KQL queries to validate hypotheses
• Pulling context from multiple screens/tools
• Copy-pasting evidence into tickets
• Repeating enrichment steps (IP reputation, user context, device context)
• Explaining the same investigation flow to junior teammates

This creates a talent bottleneck: the more experienced the analyst, the more expensive each incident becomes.

AI-driven workflows: Guided investigation at machine speed

AI-driven operations shift work from “manual investigation assembly” to “guided decision-making.”

With an AI-powered Sentinel SOC platform like SecQube’s conversational assistant (Harvey), you can:

• Triage incidents conversationally without requiring KQL expertise
• Auto-generate and run relevant KQL queries based on the incident context
• Apply standardised workflows for enrichment, evidence capture, and next steps
• Use integrated ticketing and change management to reduce tool switching
• Support multi-tenant operations (especially for MSSPs) without multiplying headcount

For reference, SecQube positions this as an AI-powered, multi-tenant platform built for Microsoft Sentinel operations, with automation and conversational investigation designed to reduce skills gaps and speed up resolution. You can explore more at SecQube.

A practical ROI model you can use (with example numbers)

Every environment is different, but ROI modelling becomes easier when you convert time into cost and scale effects.

Here’s a simple framework:

• Monthly incident volume: number of incidents requiring investigation/closure
• Average TTR: hours per incident (end-to-end)
• Fully loaded analyst cost: cost per hour (salary, benefits, overhead)
• Rework rate: percentage of incidents reopened or escalated due to incomplete triage

Example comparison table (illustrative)

Where AI wins decisively: Scale, consistency, and predictive context

AI-driven workflows tend to outperform traditional methods in three high-impact areas.

1) Handling large-scale threat volume without linear headcount growth

Traditional models scale like this: more incidents → more analyst hours → more cost.

AI-driven models scale differently: more incidents → more automated steps → analysts focus on the hardest 10–20% of cases.

This is where ROI becomes visible. When volume doubles, cost need not double.

2) Predictive insights and proactive prioritisation

When AI is paired with real-time threat intelligence and severity assessment, it can help you:

• Prioritise incidents that match active attack patterns
• Identify repeated signals across tenants or business units
• Reduce time wasted on low-risk, noisy alerts

Even small prioritisation gains pay off, because they prevent senior analysts from being pulled into false urgency.

3) Integrated ticketing that removes “workflow tax”

In many SOCs, the ticket is the incident’s “second life.” If it’s poorly updated, the same questions repeat during escalation, audits, or post-incident review.

Integrated ticketing and change management reduces:

• Context switching
• Missing documentation
• Delays caused by incomplete handoffs

This is a quiet but major contributor to cost-per-incident reductions.

User-centric AI: The hidden driver of speed and accuracy

A lot of “AI in security” efforts fail because they add complexity. User-centric AI does the opposite: it reduces friction.

User-centric AI improves outcomes by:

• Lowering the skill barrier: junior analysts can complete high-quality investigations with guided steps
• Standardising decisions: consistent playbooks reduce variability between shifts or regions
• Reducing cognitive overload: less screen switching, fewer manual pivots, clearer next actions

Accuracy improves not because AI is “magic,” but because the process becomes repeatable and less dependent on heroic individual effort.

What to measure in your environment (before and after)

If you want to prove ROI credibly, measure operational metrics for at least 4–8 weeks before and after introducing AI-driven workflows.

Recommended KPI set

• Mean and median TTR (median is often more honest)
• Analyst hours per incident (triage vs investigation vs reporting)
• Escalation rate (and time spent per escalation)
• Reopen rate/rework rate
• Incidents closed with complete evidence artefact scale, under staffing constraints, and amid acts (audit readiness), when modelling is maintained, the same questions repeat during escalations, audits, or post-incident reviews. It becomes easier when you convert time into cost and account for different analysts making
• Ticket completeness (required fields, links, evidence consistency)

A simple improvement in median TTR and reopen rate often justifies the investment—because it compounds across every incident.

When traditional analysts still matter (and how AI complements them)

AI-driven workflows don’t remove the need for expert analysts. They protect expert analysts.

Humans remain essential for:

• Novel attack chains and ambiguous multi-stage intrusions
• Business-context decisions (acceptable risk, containment scope)
• Threat hunting and hypothesis-driven investigation
• Improving detections and governance

The best ROI comes from pairing experts with AI-driven automation, so experts spend time where expertise is irreplaceable.

The ROI conclusion: AI wins when you optimise the workflow, not just the toolset

Traditional analyst-led SOCs can deliver strong security—but ROI breaks down under scale, staffing constraints, and tool fragmentation.

AI-driven workflows tend to outperform on ROI because they:

• Reduce end-to-end time-to-resolution
• Lower cost per incident by removing repetitive labour
• Improve consistency via guided resolution processes
• Scale across large environments and multi-tenant operations
• Keep ticketing, change management, and investigation in one streamlined flow

If your SOC runs on Microsoft Sentinel and you’re trying to close the skills gap while improving speed and accuracy, an AI-powered platform approach—like SecQube’s multi-tenant Sentinel SOC with conversational investigation, automated KQL generation, built-in ticketing, and Azure-hosted operations—can turn cybersecurity from a cost centre into a measurable operational advantage.

Want a fast ROI estimate template?

1. Pull last month’s incident count (only incidents requiring human handling).
2. Estimate average analyst time spent per incident (include ticketing and reporting).
3. Multiply by the fully loaded hourly cost.
4. Recalculate using a target TTR reduction (for example, 30–60%) and a reduced reopen rate.
5. Compare savings to platform cost to estimate payback period.

If you share your approximate monthly incident volume and current median TTR, I can help you draft a back-of-the-envelope ROI model tailored to your SOC.