I start with the assumption that disaster recovery for cloud services must prove it works under pressure, not on paper. I focus on tests that mimic real failure modes. I include third-party risks, backup strategies, SaaS recovery and the human side. I keep the exercises repeatable and measurable.
Begin by mapping dependencies down to the API and login level. List every cloud service, SaaS app and third-party integration that your core workflows call. For each entry record an owner, an RTO and an RPO. Make those targets concrete. For example, set an RTO of 4 hours for customer-facing APIs and 24 hours for internal reporting. Run a simple verification: disable the service account, then authenticate to simulate a vendor outage. If a vendor hosts identity, test alternative auth paths. If a SaaS app holds key data, verify export/import works and test the import into a sandbox. Document each test result in a single spreadsheet or a lightweight runbook. The Computerworld piece on real-world DR failures is blunt about hidden dependencies and audit-only plans; use it as a checklist when probing vendor claims [https://www.computerworld.com/article/4101198/why-is-enterprise-disaster-recovery-always-such-adisaster.html]. For formal guidance on plan structure and testing cadence, I follow NIST’s disaster recovery guidance to set scope and timelines [https://nvlpubs.nist.gov/nistpubs/Legacy/SP/nistspecialpublication800-34r1.pdf].
Make failovers realistic. Full failover exercises must move traffic, not just flip DNS. Use a warm standby or isolated cloud account that can run production stacks with synthetic traffic. Replay recorded requests for critical services and scale the standby to handle 10–30 per cent of peak load for the first exercise, then increase until the service meets its RTO. Verify end-to-end behaviour: background jobs, third-party calls, authentication and file storage. For databases, run point-in-time restores from the backup you depend on and measure how long the restore actually takes. Measure two numbers every test: time-to-recover and percent of user journeys that work after recovery. If either misses the target, list the exact step that failed and change the runbook.
Third-party risk is a practical problem, not a checkbox. Ask vendors for their recovery SLAs, their backup cadence, and the regions they use. Insist on answers about their own dependencies. If a vendor refuses, plan compensating controls: redundant providers, exportable data formats, or fallback manual processes. For SaaS recovery, test data exports monthly and import them into a sandbox where a small team performs normal tasks for an hour. Train one person to run that import. Train another to validate core workflows once the import is done. Make the training part of the test, not an afterthought.
Collect actionable feedback after each exercise. Run a short post-mortem within 48 hours. Capture five things: what worked, what failed, who did what, how long steps took, and an owner for fixes. Turn fixes into small, time-boxed tasks. Repeat the test after fixes. Over time aim to shrink the recover time by measurable amounts. Keep the runbooks simple sentences and live documents. I prefer checklists with commands and exact CLI snippets, not vague paragraphs.
Concrete takeaways: treat disaster recovery planning as engineering. Test with real traffic and complete stacks, not mocks. Verify vendor claims with hands-on exports and auth tests. Train named people to run imports and validate workflows. Collect measurable outcomes each run and close the loop with owners and deadlines. Make every test deliver a changed runbook or a fixed gap.
