How should security teams calculate the ROI of security automation before buying a SOAR platform?

What should go into a security automation ROI model?

A useful ROI model has to include both the cost side and the value side. Treat the automation programme as an operating investment, not just a licence purchase: platform fees, implementation effort, integrations, runbook design, tuning, maintenance, and governance all belong in the numerator. On the benefit side, quantify time saved, workload reduction, and avoided response spend.

The strongest models are built from measurable operational inputs, not vague promises. Estimate incident volume, average handling time, analyst cost, and the proportion of cases automation can safely absorb or accelerate. Then compare those savings against the real cost of owning the platform over its useful life, including change management and support overhead.

When the buying decision is about SOAR specifically, anchor the model in the workflow categories the platform will actually automate, such as triage, enrichment, case routing, approval gates, and containment. The right comparison is not “manual versus automated” in the abstract, but “what work disappears, what work becomes faster, and what new work the platform creates.” For a broader buying lens, the SOAR Buyer’s Guide is a useful companion because platform fit, pricing model, and proof-of-value planning affect the ROI outcome as much as the technology itself.

How do you turn efficiency gains into a defensible financial case?

Start with the labour dimension, because it is usually the easiest to evidence. If automation cuts repeated manual handling, your model should translate hours saved into fully loaded staffing cost, then adjust for the fact that not every saved hour becomes a hard cost reduction. Some savings are capacity gains, which matter, but they are not the same as immediate budget reduction.

Next, add response-quality benefits where they can be reasonably defended. Faster containment can reduce incident duration, lower the probability of escalation, and cut the cost of late response. If the team can show that automation shortens mean time to contain or reduces rework, that improvement should be valued as reduced operational drag, not just as a nicer dashboard metric.

For teams wanting a structured way to price those gains, NHIMG’s Security Investment and ROSI Guide explains how to handle mitigation value, sensitivity ranges, and post-investment review. The same logic applies here: if the benefit estimate is fragile, model best case, expected case, and conservative case rather than pretending there is one precise number.

What assumptions make SOAR ROI credible instead of overstated?

Credibility depends on conservative assumptions and explicit boundaries. Do not count every automated step as pure savings, because automation often shifts effort rather than removes it. Analysts still need to maintain playbooks, handle exceptions, review approvals, and investigate cases that fall outside the standard path.

It also helps to separate direct savings from risk reduction. Lower response cost, fewer supporting tools, and better analyst throughput are operational benefits; reduced breach impact is a risk benefit. Keeping those buckets separate makes it easier to defend the model with finance, security leadership, and operations stakeholders.

Where teams have enough historical data, connect the model to actual control performance rather than generic estimates. A security automation programme usually creates value when it removes repetitive work from the highest-volume, lowest-judgement tasks first. The Alert Fatigue Reduction Guide is relevant here because alert quality, suppression discipline, and enrichment quality directly affect whether automation produces usable savings or just faster noise.

Risk and Threat Considerations

Automation ROI can be overstated when organisations assume every automated workflow is safe, stable, and fully realised in production. The real risk is buying a platform that looks efficient in a demo but still depends on weak runbooks, poor alert hygiene, and manual exception handling, so the operational burden simply moves rather than shrinks.

Failure mechanism: Teams model savings against idealised playbooks and perfect adoption, then discover that tuning, edge cases, approvals, and integration maintenance consume more analyst time than expected. Poorly scoped automation can also increase exposure if containment or notification actions are triggered too broadly.

Impact: The business case becomes inflated, payback slips, and trust in automation drops after the first few exceptions or false actions. In a worst case, the platform adds complexity without materially improving response speed or cost.

Practitioner Guidance

What to verify: Build the model from your own incident data, not vendor benchmarks. Use current case volume, median handling time, escalation rate, and the portion of workflows that are genuinely automatable with acceptable risk.

Decision rule: If the use case depends on human judgement at several points, value the programme as partial acceleration rather than full replacement. If the workflow is high-volume and repetitive, the ROI case is usually stronger and easier to defend.

What good looks like: A credible SOAR business case shows conservative labour savings, explicit implementation and maintenance costs, and a sensitivity range for both adoption and incident volume. It should still make sense if benefits come in below plan.

Practitioner takeaway: The best ROI model for security automation is the one finance can stress-test, not the one that produces the biggest headline number.

Related resources from NHI Mgmt Group

• Guide: SOAR and Security Automation Guide
• SOAR Platform
• How should security leaders evaluate a human risk management platform before buying it?
• How should security teams evaluate an exposure assessment platform before buying it?
• How should security teams calculate the true total cost of MFA before buying a solution?