As technology constantly changes and your IT estate becomes more complex, traditional disaster recovery models will no longer be fit for purpose. While legacy disaster recovery systems and processes once provided a necessary safety net, they are no longer sufficient for the speed and scale of today's IT disaster recovery needs. Enabling your disaster recovery team with the right AI tools is essential to reducing downtime and avoiding negative impacts to your business and customers.
Rethinking legacy disaster recovery in the age of complexity
For years, legacy disaster recovery in practice has been characterized by outdated scripts, reactive recovery handling, and dense, manual runbooks. These processes rely heavily on individual knowledge, pre-written steps that quickly become out of date, and lengthy human-led coordination. In a simple, on-premises world, this approach was manageable.
Today, however, IT landscapes have vastly outgrown these systems. The explosion of distributed environments, multi-cloud setups, and interconnected SaaS platforms means a single failure can cascade rapidly. Replacing legacy DR plans requires moving beyond simple automation to embrace the ability of AI to augment teams and give them a greater edge.
What makes AI-powered disaster recovery different?
AI-powered disaster recovery represents a fundamental shift from reactive to proactive resilience. It brings capabilities that manual methods and basic automation simply cannot match. Here’s how AI is transforming IT disaster recovery:
- Rapidly creating DR plans: Use AI to generate disaster recovery runbooks in seconds from structured and unstructured data.
- Improving existing DR plans: Smart AI assistants can help to make improvements to the recovery runbook flow and reduce recovery times.
- AI agents that act as another team member: Unlike rigid scripts, AI agents can intelligently perform certain tasks, freeing up the people involved in the recovery from manual or repetitive actions.
- Predicting the next best action: AI-powered disaster recovery uses machine learning to analyze real-time telemetry and learn from training tokens from previous recoveries, and then predict the next best actions to take.
- Continuous learning from historical incidents: The AI model learns from every successful recovery, failed test, and executed incident command, automatically helping to optimize the response for next time.
What can go wrong when switching to AI-powered disaster recovery
AI-powered IT disaster recovery has the potential to provide great productivity benefits but there are some concerns to consider, such as:
- Data privacy or security risks: To be effective, AI tools require access to sensitive, high-volume data, creating potential risks such as creating an expanded attack surface for potential hackers, data leakage or misuse, and compliance violations if this data is not properly anonymized or secured.
- The need for human oversight: AI is a tool, not a replacement for human judgement, especially in high-stakes DR scenarios. Humans must be in the loop to validate AI-recommended actions before they are executed, handle unforeseen events that AI does not have the right training data to handle, and take ethical responsibility and accountability for the actions taken.
- A lack of trust in AI or inability to explain how it is reaching its conclusions: This is a major barrier to adoption in mission-critical disaster recoveries. Many powerful AI models operate as “black boxes”, which can make IT teams hesitant to trust its decisions. Plus, if an AI-driven automated recovery fails, it can be extremely difficult to debug the issue because the underlying AI logic is opaque, hindering post-incident reviews and continuous improvement.
- Regulatory or compliance risks: The transition to AI-power disaster recovery introduces a new layer of complexity and organizations need robust documentation proving that the AI is correctly configured, tested, and that actions are auditable. Financial institutions, healthcare providers, and others have strict rules about system resilience and data handling. An AI failure could result in non-compliance, leading to major fines.
Best practices for AI-powered disaster recovery that actually work
Adopting the following best practices for AI-powered disaster recovery will ensure a smooth, confident migration to an intelligent recovery framework.
Treat DR as a living system
Avoid static runbooks. Your recovery process must be as dynamic as your environment. Use AI capabilities not only for execution but for continuous testing and refinement, allowing the process to evolve and adapt to new environments automatically.
Blend human judgment with machine precision
Don't chase full automation for its own sake. The most resilient systems use AI-powered runbooks to handle repetitive, high-volume tasks, while setting clear, well-defined thresholds for when humans must intervene. This keeps human expertise at the critical decision points while maintaining the speed and precision of the machine.
Building disaster recovery into your team’s core capabilities
The true payoff of IT disaster recovery software comes from organizational transformation, not just tooling upgrades.
- Skills: Upskilling SREs and IT operators in AI observability and data science fundamentals is key. They need to understand the logic behind the automated recovery flows.
- Culture: Encourage low-stakes AI experimentation through regular "game days." These build confidence in automated disaster recovery scenarios before a real event.
- Metrics: Track success metrics, such as reduced recovery times and number of tasks successfully executed by AI.
Using Cutover's AI-powered automated runbooks to modernize disaster recovery
Cutover Recover utilizes runbook automation software and AI to transform static recovery plans into dynamic, executable workflows. By leveraging AI-powered automated runbooks, teams can accelerate runbook creation, intelligently suggest improvements, and execute multi-system recoveries with real-time orchestration and auditability, providing the foundation for true AI-powered disaster recovery.