Why does SOAR maintenance become so expensive at scale?

Why SOAR Becomes an Engineering Platform at Scale

SOAR is inexpensive when it is treated as a thin automation layer over a stable set of alerts, systems, and decisions. It becomes costly when the environment changes faster than the automations can absorb it. At that point, the platform is no longer just executing playbooks, it is also absorbing integration drift, exception handling, and repeated rework across teams.

The key shift is that scale multiplies variation. Every new tool, alert source, queue, approval path, and exception case creates more conditional logic, more testing, and more dependency management. The automation only looks “set and forget” until the underlying security stack, ticketing model, or operational workflow changes enough that each runbook needs active maintenance.

That maintenance burden grows because SOAR content is often tightly coupled to the environment it was built for. A playbook that works cleanly for one SIEM rule, one endpoint product, or one case management flow can break when field names change, a vendor API changes behaviour, or analysts begin handling the incident differently. In practice, the cost is not the first build, it is keeping the build current.

Where the Cost Comes From in Practice

Most of the expense sits in ongoing engineering work: updating connectors, validating data mappings, adjusting branching logic, and re-tuning human approvals so the automation still fits reality. Reporting adds another layer because teams usually need evidence that the automation is reducing effort, not simply moving work into a different queue. That means metrics, dashboards, and exception reports also need maintenance.

Scale makes this worse because the long tail matters. A few core playbooks may remain stable, but the majority of operational friction usually comes from edge cases, partial failures, and integrations that only appear when volume rises. Once teams must maintain a catalogue of “known exceptions” and special handling paths, the operating model starts to resemble application support, not lightweight automation.

Tooling churn is another cost driver. Security teams often replace scanners, enrichers, ticketing systems, chat platforms, or identity workflows over time, and each replacement can invalidate assumptions buried inside playbooks. If the SOAR layer is not abstracted enough, every downstream change becomes a custom engineering task rather than a simple configuration update.

Why the Economics Deteriorate as Automation Grows

SOAR economics deteriorate when the organisation expects the platform to keep delivering value without continually pruning, simplifying, and retiring stale automations. If no one owns playbook hygiene, every new alert type creates another maintenance obligation, and old logic accumulates even after the original business need has changed. The result is operational drag, not compounding efficiency.

At scale, the hidden cost is governance. Automation that can approve, route, close, or enrich incidents needs periodic review for accuracy, access boundaries, and business impact. If those checks are skipped, the platform may still function technically while producing poor operational outcomes, such as noisy triage, duplicate case creation, or incorrect escalation routing. NIST Cybersecurity Framework 2.0 is useful here because it frames automation as part of an ongoing govern, identify, protect, detect, respond, recover cycle rather than a one-time deployment.

Maintenance also becomes expensive when teams over-customise the platform to mirror local processes. The more the automation depends on bespoke logic, the more every organisational change becomes a code change. That is why mature programmes tend to standardise triage patterns, reduce one-off branches, and keep the number of unique playbooks lower than the number of alert types they support.

Practitioner Guidance

What to prioritise: Treat playbook sprawl, connector fragility, and exception volume as the real cost centres, not license spend. If a workflow needs frequent manual repair, it is already an engineering service and should be managed like one.

What to verify: Check how many automations depend on brittle field mappings, vendor-specific APIs, or manual approval steps. The highest-maintenance SOAR content is usually the content that is most tightly coupled to unstable upstream data.

Trade-off: The more precise and automated the workflow, the more care it needs when the environment changes. Organisations often buy speed in the short term and inherit a standing obligation to keep that speed from degrading.

Practitioner takeaway: SOAR is cheapest when it removes repeatable work from a stable process; it becomes expensive when it must continuously absorb process, tool, and alert drift across a changing security stack.

Related resources from NHI Mgmt Group

• How should security teams govern non-human identities at scale?
• When does a machine identity become a compliance problem?
• When does secret exposure become a broader identity risk?
• When does NHI compliance become an operational security issue?