Business Continuity in the Age of AI

[Eugene Chuvyrov]

Even in the age of AI and intelligent agents, the core objectives of Business Continuity and Disaster Recovery (BCDR) remain unchanged: ensuring that mission-critical systems continue to operate and can recover quickly in the face of disruption. What has changed are the challenges: keeping AI systems resilient raises issues that are very different from those that affect virtual machines, databases, and other infrastructure. In this post, we explore the key pillars of ‘AI-native’ continuity planning, highlighting both the similarities and the emerging differences between the BCDR strategies of yesterday and those required for AI-native enterprises of tomorrow.
 

1. Rethink Failover and Failback for AI 
   Traditional disaster recovery has long centered on servers, storage, and business applications. These IT assets could usually be backed up, replicated, or restored from a known state. In the AI era, however, the “system” at risk is no longer a static database or application but instead a set of large language models and applications built on top of those models, a constantly evolving machine learning pipeline, or even a distributed workforce of AI agents acting across multiple domains. These systems could be very fragile: an outage, corrupted dataset, or poisoned model can have effects far beyond simple downtime. The concepts of failover and failback must evolve to include these new fragile domains.
   
   Failover now means more than spinning up standby servers. It involves detecting disruptions in model serving, activating contingency systems such as fallback models or cached embeddings, redirecting AI-bound workloads to alternative endpoints or providers. It is also now about verifying not just availability but output quality and trustworthiness. 
   
   Failback similarly goes beyond just restoring the primary system. It may require resynchronizing model states, retraining on clean data, recalibrating agent coordination, and validating that the restored system produces consistent and reliable outcomes that match the outputs before corruption or outage. Therefore, having an extensive set of metrics and validation data to work with becomes a critical bit for BCDR in the Age of AI. 

2. Protect Data Integrity and State 
   In traditional IT systems, “state” often meant the contents of a database or a file system. In AI systems, the state is both richer and more volatile, it now encapsulates embeddings that capture knowledge, partial outputs from long-running agent workflows, fine-tuned model weights, reinforcement learning progress, and even curated prompt libraries. Losing a day of this state is not equivalent to rolling back a transaction log. Instead, it can represent hundreds of human-days of annotation, iteration, and refinement that cannot simply be re-entered from memory. Corrupted or missing state may slowly degrade model performance without immediately revealing that anything is wrong. To continue operating as smoothly as possible, businesses should take time to ensure the following actions are taken across the AI landscape: 

• Snapshots and checkpoints: Regularly save model parameters, embeddings, and workflow progress so systems can be rolled back quickly. 
• Replay mechanisms: Keep detailed logs so agent activity or training runs can be replayed to rebuild lost progress. 
• Versioned storage: Store models, fine-tunes, and workflows in versioned repositories to make recovery and auditing straightforward.
  
  Preserving AI state must be treated as mission-critical, on par with business financial ledgers or healthcare patient records. Nightly backups are no longer sufficient; instead, business continuity requires ongoing protection of dynamic AI states to avoid loss of productivity, accuracy, and trust. 

3. Demand SLAs and Plan for Vendor Risk 
   Hyperscalers may advertise “four nines” of uptime, but many AI providers, especially many startups emerging in the AI space, cannot yet offer these enterprise-grade guarantees. Without clear service commitments, your AI-powered workforce may be far more fragile than expected. To counteract this, consider the following guidelines when assessing vendors for AI BCDR readiness: 

• Set explicit SLAs for model uptime, latency, and recovery time. 
• Assess vendor resilience by reviewing multi-region redundancy and failover practices. 
• Diversify providers to reduce lock-in and mitigate concentrated risk.
  
  Don’t just buy AI but instead demand enterprise readiness from all AI vendors. 

4. Build Hybrid and Multi-Cloud Resiliency 
   Enterprises rarely rely on a single cloud provider, and the same principle should apply to AI. A multi-cloud strategy provides a buffer against outages, vendor failures, or sudden policy changes. The following guidelines provide basic concepts for cloud resilience: 

• Designate a primary and secondary provider (e.g., Azure OpenAI with Anthropic or AWS Bedrock as warm standby). 
• Balance cost against protection, treating redundancy as an insurance investment rather than overhead. 
• Verify compliance across environments so that failover does not compromise regulatory obligations.
  
  Multi-cloud AI isn’t a luxury; it’s a part of an overall resilience plan. 

5. Don’t Overlook Critical Risks 
   AI continuity requires attention to risks that traditional IT planning often ignored because it wasn’t critical. However, failure to address these risks in the age of AI can undermine not just availability, but trust, compliance, and business value. Here’s a small subset of these new risks: 

• Model and pipeline risks: Guard against corrupted fine-tunes, data poisoning, or misconfigured workflows. Reviewing the OWASP LLM Top 10 and NIST AI RMF frameworks and ensuring there are plans in place to address issues highlighted in those frameworks is now critical. The OWASP Top Ten for AI outlines the most common security vulnerabilities, while the NIST AI Risk Management Framework offers structured guidance for identifying, assessing, and mitigating risks across the AI lifecycle. Establishing clear plans to address these areas has become a key requirement for AI Deployment and a crucial pillar for the AI BCDR strategy. 

• Regulatory continuity: Ensure recovery processes uphold GDPR, HIPAA, and other mandates. 
• Dependency chains: Account for vector databases, orchestration frameworks, external APIs and many other rapidly evolving AI constructs. 
• Testing and drills: Continuity plans are only proven when exercised under real conditions. 
  
  AI BCDR must cover the entire dependency chain, not just the servers and GPUs beneath it. 

6. The Human Element 
   Even in an AI-driven enterprise, people remain the most important and final line of resilience. Business Continuity planning must ensure: 

• Education and Expertise: Staff must understand AI pipelines, data flows, and recovery steps so they can restore systems to a trusted state. 
• Motivation and Oversight: A motivated workforce that feels empowered to work with AI will step up during crises, providing judgment and validation that machines cannot. 
• Human Substitution: In the event of AI failure, people may need to temporarily perform AI’s role, whether reviewing data, making decisions, or executing processes to keep operations moving. 
• Redundancy of Skills: Cross-training prevents reliance on a few specialists and ensures recovery efforts continue even if key personnel are unavailable.
  
  Humans are not a fallback; they are an integral component of a well-functioning AI-native enterprise. Educated, motivated, and prepared teams can bridge gaps when AI falters, ensuring continuity of both systems and business operations. 

Conclusion 
AI is no longer experimental. It has become foundational to business operations. Yet without robust continuity planning, organizations risk disruption, reputational damage, and even operational paralysis when AI systems fail. Business leaders must now broaden their BCDR perspective beyond servers and data centers to encompass models, pipelines, and intelligent agents. 

By securing SLAs, broadening backup strategies, preserving trust and testing recovery plans through regular drills, leaders can ensure that AI delivers not just business results, but resilient innovation, securing competitive advantage over enterprises who don’t implement those plans or implement them in a less resilient manner. 

It's time to ask your teams: If our AI went down tomorrow, how would we continue to serve customers innovatively and keep the business running?