Your Disaster Recovery Plan Is Outdated. Here’s How AI Can Fix That.

Across organizations, disaster recovery runbooks have historically played the role of a failsafe to be recalled in the event of a data-related incident. Some security teams even treat it as a kind of insurance policy to showcase that they have prepared themselves to deal with unforeseen events.

These runbooks typically include detailed procedures and protocols, and are largely never tested out except maybe during annual security audits. Invariably, they become obsolete, especially in today’s hyperconnected world, where data infrastructure in most organizations is increasing both in size and complexity.

While organizations can enforce a regular cadence for validating disaster recovery (DR) runbooks, most shy away from such actions owing to a shortage of either time or resources.

However, as AI technology has matured, we now have an opportunity to leverage its prowess to transform these static runbooks into continuously validated and robust SOPs.

Why outdated runbooks are an invitation for financial and reputation loss

If you take a look at traditional disaster protocols in most organizations, you can quickly observe a laid-back working paradigm. The technical team, with the help of business functions, takes time to build a comprehensive runbook. Once it’s drafted and approved, it rarely sees the light of day except during annual tests.

Even during audits, many organizations perform just a dipstick scenario check and do not involve extended teams to test out real-life scenarios. The documentation gets updated and packed up till the next annual audit. This ineffective routine exposes critical vulnerabilities.

In a technology ecosystem where cloud migrations and microservices dominate, the underlying infrastructure can change rapidly; annual DR runbook updates can cause catastrophic losses. In the event of a disaster, teams would be at their wits’ end if they find their runbook referencing decommissioned servers or outdated outflows.

The delay in recovery resulting from such a scenario and possible data loss can lead to organizations losing a ton of money. Compliance and reputation risks can add up staggering costs and leave leaders scrambling for answers.

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How AI fits into the disaster recovery paradigm

With AI at your disposal, planning for disaster recovery and preparing the related documentation undergoes a complete shift. Instead of a one-time activity, it becomes a continuous and proactive process. AI systems, once ingrained in your organization, can be used to continuously monitor your technology infrastructure and data workflows. They can observe and update potential scenarios in your DR runbooks on the fly.

For example, when a new migration happens, or a configuration or application dependencies get changed, AI can instantly update the runbooks. This helps in bridging gaps between actual workflows and those documented in DR runbooks that are typically noticed in a traditional setup.

Ensure continuous testing without downtime

One of the fundamental advantages of using AI in disaster recovery planning is its capacity to continuously test scenarios without enforcing downtime. AI models can effectively simulate real-world failure scenarios based on system health, user actions and even based on incidents occurring in other organizations.

It can then simulate interventions in a non-production environment and check how it plays out. Results can be shared with the tech team, countermeasures can be updated in the production system, and protocols can be updated in DR runbooks.

Refining DR runbooks

An interesting benefit of using AI is its capacity to update overly technical and difficult-to-follow documentation to what actually needs to be done in a recovery scenario. As infrastructure and workflow changes happen in the organization, AI can generate easy-to-follow sections that even operations executives can execute during a data incident.

Moreover, AI helps in maintaining continuity between different associated runbooks and ensures changes made in one are cascaded to other relevant ones in real-time.

A sneak peek into the future

We are looking towards a future where AI actively analyzes the health of technology infrastructure in organizations, makes sense of different metrics from upstream and downstream systems and even factors in user actions. Together, it makes predictive DR a reality where it can forecast any incidents of failure before they occur.

Add to it, AI can enable autonomous recovery without the need for human intervention. Organizations can classify scenarios where AI can automatically get things back in shape while keeping the most complex scenarios under the purview of humans.

It is often said that no force can stop an idea whose time has come. The idea of integrating AI in keeping DR runbooks updated falls squarely in the same bracket. The benefits completely outweigh any resistance to the idea from technical teams or financial bean counters.

AI not only prevents your DR runbooks from becoming a relic, but it also dramatically improves the recovery outcomes. Continuous validation also helps in increasing operational confidence, and teams can initiate changes without breaking a sweat over the possibility of data mishaps.

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