They often compare licensing cost to analyst savings and ignore the engineering labour needed to keep automation running. That misses connector maintenance, playbook rework, and the opportunity cost of senior staff debugging workflows. A realistic ROI model must include all three or it will systematically understate the true cost.
Why SOAR ROI Is Usually Misread by Security Teams
SOAR is often sold or evaluated as if the economic question is simple: automation licence cost versus analyst hours saved. That framing is too narrow because it treats orchestration as a one-time purchase rather than an operational capability that must be engineered, maintained, and governed. For teams that already struggle with alert quality, brittle integrations, and shifting response workflows, the hidden work can dominate the financial picture. NHI Management Group sees this most often when organisations measure initial deployment success but do not track the cost of keeping automations aligned to real incidents and changing tool behaviour.
For a practitioner view of the machine-identity and credential exposure that often sits behind automation sprawl, the OWASP Non-Human Identity Top 10 is useful because SOAR workflows frequently depend on API keys, service accounts, and other non-human access paths.
In practice, many security teams discover the true cost of SOAR only after connector drift, brittle approvals, or workflow breakage has already forced repeated manual intervention.
What the Automation Actually Costs Once It Leaves the Pilot
A realistic SOAR ROI model has to include the engineering work needed to keep the automation trustworthy. That includes building and testing integrations, updating playbooks when upstream products change, and handling exceptions when an automated step does not match the live incident. The return comes from reduced repetition and faster routing, not from eliminating human judgement altogether.
The most common mistake is to assume that every alert tied to a playbook becomes a net labour saving. In reality, some use cases are net positive only when the decision path is stable, the data quality is predictable, and the response action is low-risk. If the workflow requires frequent edits, manual approvals, or post-failure cleanup, the ongoing operating cost can erase the expected gain.
- Connector maintenance matters because SOAR value depends on external systems staying stable.
- Playbook rework matters because detection logic, ticket fields, and response authority all change over time.
- Senior engineer time matters because debugging failed automations is expensive and hard to scale.
- Exception handling matters because real incidents rarely follow the clean path used in demos.
SOAR also creates an operational dependency: the more a team automates, the more it must monitor the automation itself. That means ROI should be measured over the full lifecycle, not just the first months after launch. A good model separates gross efficiency from net efficiency and tests whether the process still saves time after support, maintenance, and revalidation are included.
This guidance breaks down when the organisation cannot observe workflow failures, because invisible breakage makes any ROI claim look better than it is.
Where SOAR Economics Break Down in Real Environments
Tighter automation often improves consistency, but it also increases maintenance overhead, so teams have to balance speed against brittleness. The trade-off becomes sharper in complex environments where tools are diverse, controls are not standardised, or incident types vary widely. In those settings, a SOAR use case may look efficient in a controlled test but deliver poor net value once exceptions become routine.
There is also a genuine consensus gap in the market: some organisations treat SOAR as an analyst efficiency platform, while others use it mainly as a control-enforcement layer for repetitive containment actions. Those are not the same business case. If the goal is analyst productivity, the primary metrics should be time saved and queue reduction. If the goal is control consistency, the primary metrics should be response reliability, decision quality, and reduced manual variance.
Questions about return on investment also change when SOAR depends on privileged integrations, automated remediation, or external data enrichment. In those cases, failures are not just cost issues. They can become access-control issues, availability issues, or false-confidence issues if the automation is trusted more than it should be. Teams should therefore treat unstable workflows as a governance signal, not just an efficiency problem.
Security teams get the calculation wrong when they price the licence but not the operating model, because automation that cannot be maintained at scale is not a savings programme, it is a new support burden.
Risk and Threat Considerations
SOAR introduces operational and governance risk when teams assume that automated playbooks will remain correct after upstream tools, permissions, or detection logic change. It also creates exposure when automation holds privileged access or can execute containment actions without enough human review. In that state, the risk is not only wasted spend but also mis-execution, service disruption, or over-trusted response paths.
Failure mechanism: brittle connectors, stale credentials, incomplete exception handling, and untested workflow edits can cause automations to fail silently, execute the wrong action, or require repeated manual override.
Impact: organisations lose the expected efficiency gain, absorb ongoing engineering overhead, and may create new availability or access-control exposure if automated actions run with excessive authority.
Standards & Framework Alignment
This section maps relevant standards and security frameworks to the operational risks and controls described in this guidance.
OWASP Agentic AI Top 10 and OWASP Non-Human Identity Top 10 address the attack and risk surface, while CIS Controls v8 and NIST CSF 2.0 set the governance and control requirements practitioners need to meet.
| Framework | Control / Reference | Relevance |
|---|---|---|
| CIS Controls v8 | CIS 16 — Application Software Security | SOAR relies on workflows and integrations that need secure change control. |
| CIS 12 — Network Infrastructure Management | SOAR connectors and dependencies require controlled, monitored operational support. | |
| Recommendation — Apply CIS 16 to keep automations tested and resilient as integrations change. Use CIS 12 to reduce brittle dependency drift across connected security tools. | ||
| NIST CSF 2.0 | GV.SC — Supply Chain Risk Management | SOAR value depends on external platforms, connectors, and service dependencies. |
| ID.IM — Improvements | SOAR playbooks require continuous improvement when incidents reveal workflow gaps. | |
| PR.PS — Platform Security | Automated response quality depends on secure, stable platform and connector operation. | |
| Recommendation — Map SOAR dependencies to GV.SC and review third-party and integration risk routinely. Use ID.IM to update playbooks after failures and operational lessons. Apply PR.PS to harden the automation platform before expanding response scope. | ||
| OWASP Agentic AI Top 10 | A2 — Tool and Action Governance | SOAR automations execute actions through tool access and need governed authority. |
| Recommendation — Use A2 to constrain which automated actions can run without human approval. | ||
| OWASP Non-Human Identity Top 10 | NHI-03 — Secrets and Credential Management | SOAR workflows commonly depend on API keys, service accounts, and tokens. |
| Recommendation — Apply NHI-03 to control and rotate the credentials used by automation workflows. | ||
Practitioner Guidance
What to prioritise: Build the ROI model around the use case lifecycle, not the deployment event. The first question is whether the workflow will stay stable enough to justify automation ownership, testing, and rework.
What to verify: Validate how often playbooks change, how many connectors are business-critical, and how much senior engineering time is spent on failures or exceptions. If those costs are not measurable, the ROI claim is not reliable.
What good looks like: A healthy SOAR programme shows repeatable use cases, low exception rates, clear ownership for workflow maintenance, and a net reduction in manual handling after support costs are included.
Practitioner takeaway: Treat SOAR as an operating capability with ongoing maintenance economics, not a static product purchase, because the real return depends on whether automation remains dependable after the first version ships.
Related resources from NHI Mgmt Group
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Reviewed and updated by the NHIMG editorial team on September 7, 2026.
NHI Mgmt Group — the #1 independent authority on Non-Human Identity, IAM, and Agentic AI security. nhimg.org
