Everybody everywhere is experimenting with, or at least talking about, AI. It’s touted as having the power to transform various aspects of businesses, such as productivity and customer service. However, while AI may not be a cure-all solution, there are specific areas where AI is expected to bring significant change.
One such area is business continuity and disaster recovery (BCDR), which have traditionally relied on multiple tools, processes, and plans that require constant testing and refinement.
“AI has a lot of potential in these areas because it can gather information about what’s going on in an environment, parse through logs, and suggest a path for resolution in plain English,” said Christophe Bertrand, a senior analyst at Enterprise Strategy Group.
What Are the Benefits of AI in BCDR?
AI offers a multitude of benefits to business continuity and disaster recovery.
Firstly, AI can greatly enhance BCDR plans by improving their organization and providing guidance. Whether companies are rewriting their existing plans or creating new ones, AI can be put to work to analyze ISO standards, professional best practices, and other relevant documents, creating a solid starting point for the plan’s development.
Jason Rubens, a senior project manager for Strata Results Group, highlighted the effectiveness of generative AI in evaluating BCDR plans. In a blog post, Rubens explained that he asked ChatGPT to assess a plan against the Federal Financial Institutions Examination Council (FFIEC) standard. ChatGPT absorbed the FFIEC Business Continuity Planning Booklet and identified potential gaps and areas of improvement.
“That’s pretty amazing, especially when you consider how much work it would be for a person to extract this kind of information from a standard or review a plan for gaps,” Rubens said in the blog post.
AI also has valuable applications in analyzing business impacts and conducting risk assessments. Not only can AI analyze machine logs and other relevant documentation, but it can even identify interdependencies in data that could cause problems over time.
When disaster strikes, the timely and accurate dissemination of information is critical, and AI can contribute to speeding up this process. “One of the big problems people have at times of disaster is getting accurate information, not getting incorrect information or rumors, or getting duplicate information,” explained Betty Kildow, a business continuity management consultant at Kildow Consulting. “When data is coming in from so many sources, AI can really help in sorting it much faster and better than people could.”
In addition, AI has broad applications in decision-making scenarios, including incident response. It can evaluate various aspects of the incident, such as its impact, and then make recommendations about recovery and evacuation if necessary. That makes it ideal for predictions, as well, as AI can offer decision-makers a high-level perspective of current and potential risks.
Another important use case for AI is automated testing, modeling, and exercising of business continuity and disaster recovery plans. For example, AI can suggest specific scenarios for testing or exercising plans and trigger changes in the sequencing of activities.
Finally, AI can be helpful in the recovery of both business operations and technology. On the business side, AI can initiate incident-related actions like contacting staff members and deploying devices at an offsite location. Similarly, on the technology side, AI can enable failover procedures and ensure that technology resources are ready to go from remote locations or the cloud.
In all these applications, AI is actively working toward enhancing resilience in the environment and making data more manageable.
“[AI] is feeding into the bigger story around business intelligence and analytics on the one hand and more integration with cybersecurity resilience processes through automation on the other,” Bertrand said.
AI Is a Tool, Not a Magical Solution
While AI can make a big difference in both BCDR operations and management, its effectiveness relies on the availability of accurate and quality data inputs.
This doesn’t always happen. “AI isn’t magic. You need to program it, take care of it, and give it quality data,” said Bobby Williams, former senior manager of disaster recovery for Advance Auto Parts and currently business continuity/team leader at a cybersecurity consultancy. “For example, if you document an outage by tagging it as a ‘summertime high-usage power outage,’ it’s not going to deduce anything other than that it was a power outage.”
In short, the quality of inputs determines the quality of outputs. “As amazing as it is, AI at its core is a tool,” Kildow said. “It’s a hammer, and we still need to manage the hammer.”
To determine if AI can benefit a particular organization, a small-scale implementation can serve as a comparison point with what you already have in place. Kildow suggested putting AI to work on a risk assessment – a task most organizations have already undertaken. Evaluating the output of the AI tool and comparing it to existing assessments can reveal its effectiveness.
The bottom line is this: AI is still evolving and undergoing refinement. Over time, as the technology matures and its benefits and limitations become more apparent, AI may see acceptance and integration into standard practices.
“We saw the same thing with business continuity software early on,” Kildow said. “When it first came out, there was the same pushback I’m seeing with AI today. That’s something that comes with new technology, but it will be accepted over time.”
About the author
Karen D. Schwartz is a technology and business writer with more than 20 years of experience. She has written on a broad range of technology topics for publications including CIO, InformationWeek, GCN, FCW, FedTech, BizTech, eWeek and Government Executive.
