Business continuity in the age of AI dependency

 The rise of artificial intelligence (AI) has transformed the way businesses operate, offering unprecedented efficiency, scalability, and innovation. From predictive analytics to automated decision-making, AI has become integral to critical business processes across industries. However, this growing dependency on AI in the various forms it is operated and used, introduces unique risks that traditional business continuity plans may not be addressing effectively in its current form.

System fragility, adversarial attacks, and data vulnerabilities are just a few examples of challenges that can jeopardize continuity in AI-dependent services, and eventually, AI-dependent businesses.

To ensure resilience, businesses must adapt their continuity planning to account for AI-specific risks, establish robust continuity solutions, and assign adequate human oversight to enable teams to catch issues at their early stages. To start thinking about expanding your current business continuity planning and protect the growing AI-dependent aspects your organization is undertaking, this article brings you the key strategies for creating a resilient plan even as AI dependency inevitably continues to grow.

The New Focus: AI-Specific Risks

AI systems, while powerful in some ways, are not infallible on the tech stack level. Their reliance on algorithms, APIs, varied data sources, and, at times, complex infrastructure, makes them susceptible to unique failures. Here are some challenges businesses must address to prioritize continuity:

AI System Fragility: AI systems often struggle with "edge cases" or scenarios outside their training data, which can lead to unpredictable behavior, and therefore ‘break’ easily, producing negative results instead of those originally intended.

Common and AI-Specific Security Vulnerabilities: AI systems are particularly vulnerable to adversarial attacks, where malicious actors manipulate data to exploit system weaknesses. This is true for the infrastructure that underpins AI implementations, as it is true for the data used by AI systems, the APIs that connect them to data, and the applications themselves that people use every day.

AI Data Risks: AI systems depend on vast amounts of data, making data integrity and security critical for their operation, but not easier to protect, especially at the scale that AI systems require.

To mitigate these key risks, organizations should start mapping and integrating more AI-specific considerations into their business continuity solution design, solution selection and planning for both incidents and crises.

Key Strategies for a Resilient Business Continuity Plan

Assess AI Dependencies

Mapping AI dependencies is the first step toward understanding how critical processes rely on AI systems. Mapping the people who are tasked with operating them, and those governing the data they consume, is even more critical. Nowadays, many organizations are already appointing a CAIO – Chief AI Officer, and this new role oversees business and technical roles you should interact with to get an accurate picture of dependencies, and then find gaps that may require new strategies and solutions.

-          Meet with the relevant Business Operations, Risk & Compliance, and technical teams to identify workflows and systems that depend on AI.

-          Pinpoint areas most vulnerable to AI failures, whether due to potential technical issues, single points of failure, data issues, or risk due to cyber attacks.

-          Evaluate the likelihood and impact of failure points within these dependencies so you can make the right business case for the organization’s leadership.

Looking at your existing Risk Register, chances are that you would need to make considerable updates, if those are not already made. By creating a detailed AI reliance map, you can think of the right ways to prioritize areas that require immediate action and proactive risk mitigation.

Updating the Business Impact Analysis (BIA)

While AI systems may rely on common infrastructure, data sources, and networking as other workloads, overlooking AI-specific scenarios, can leave organizations unprepared for disruptions. Your current BIAs may need updating per the extended Risk Register, making sure to adapt BIAs to new business activities and processes that have been deployed since the last BIA.

Start with business process owners first to understand criticality, then work backwards to technical requirements. AI systems often have unique BC considerations around model drift, training data dependencies, and performance degradation that traditional BC planning doesn't address.

-          As you update the BIA, include scenarios for AI system failure, such as algorithmic errors, adversarial attacks, or data breaches.

-          Evaluate the impact of these scenarios on critical Products & Services, Business Activities, and Business Processes, according to the way your organization carries out their mission.

-          Review/assign continuity objectives to categorize:

o   Recovery Time Objectives (MTPD, RTO) How quickly must the AI system be restored?

o   Recovery Point Objectives (RPO) How much data/learning loss is acceptable?

o   Minimum Business Continuity Objectives (MBCO) What is the performance degradation or reduced functionality acceptable for this system?

Implement Workarounds, Fallback Procedures

AI systems are designed to operate autonomously, but human intervention remains essential in the face of system failure. Fallback procedures for AI should establish scenarios and protocols where human teams should be triggered to take over if/when an AI system fails. Think through these scenarios, and ensure backup systems and manual processes, as well as those in your team tasked with taking charge, are ready, and documented in your plans. Having these in place, can help maintain a certain level of operations during outages / cyber crises that impact on AI workloads or customer facing activities.

Extend Planning with AI-Specific Vendor SLAs

Business Continuity that your organization can control extends well beyond organizational walls. For most companies who are taking their first steps in the world of AI for business, AI vendors play a crucial role in maintaining data and system reliability. It goes without saying that robust service-level agreements (SLAs) can help mitigate some of the risks associated with potential outages and other issues. Review contractual language to ensure that it demonstrates shared accountability for continuity planning, covers relevant KPIs, and details recovery support you can expect in case of an incident/crisis.

Test, Review, Optimize Over Time

Test the Technology: Stress-testing AI systems helps businesses understand their limitations and prepare for worst-case scenarios. Evaluate AI system behavior under pressure and identify potential failure points.

Test the Humans: Once plans are in place and all roles have been assigned, start training staff to respond to AI-specific incidents. If there are Tier 1 systems that rely on AI, then routine incident simulations for AI-specific failure scenarios is definitely an exercise to schedule.

Use the results from testing and exercises to continually update and evolve plans. Continuous optimization ensures business continuity measures remain effective and relevant in an ever-changing AI landscape, no matter the source of an incident.

AI Resilience is Business Resilience

Today, businesses increasingly depend on AI for a an increasing variety of operations, and many are already minimizing their workforce as a result. In the next 5-10 years, we are bound to see a fully integrative workforce made up of both humans and autonomous Agents working together as part of BAU operations. It is therefore imperative that continuity planning evolve as a top priority at this point to address risks that can impact AI systems in both generic and more unique senses.

AI dependency is no longer a future challenge—it’s a present reality that demands action at the highest levels of your organization. Take this topic to your executive team and start the conversation about prioritizing business continuity in the age of AI. By driving a culture change that emphasizes proactive risk management and resilience, your leadership can ensure the organization is prepared to navigate AI disruptions and safeguard its future. The time to act is now—make AI continuity planning a strategic priority for your business!