Using AI for business disaster response
How is AI being used for business disaster response?
Business disaster preparedness, and response plan strategies can be greatly aided by artificial intelligence. The use of artificial intelligence can provide business continuity an edge by automating IT service management, automated data recovery, and planned disaster recovery. In addition to monitoring emerging trends, AI is also being used to develop more sophisticated tools, such as identifying, quarantining, and removing potentially compromised data after a security breach, or building an emergency notification system. The predictive analytics capabilities of AI give a great competitive advantage in supply chain scenarios such as monitoring cyber-attacks, managing company brands & reputations, monitoring market trends or looking at satellite imagery continuously.
What are the technology's benefits and drawbacks?
Using artificial intelligence and machine learning for disaster recovery, and automating processes to accelerate incident response, and gaining predictive insights to manage disaster recovery are the principal benefits of these technologies, however it is not without challenges.
The limited amount of sufficient and pertinent training data, latent event correlation due to lack of alternative data sources, anomalies & outliers, and unforeseen events (black swans) cannot be predicted with high confidence; therefore, human intervention and keeping adaptive learning in the loop is imperative.
What are some top AI disaster response tools?
Oracle Risk Management Cloud, One Trust, and Fusion framework systems are some of the key business continuity management tools that help organisations maintain operational reliability and resilience. Some of them claim to use advanced analytics capabilities.
How will AI business disaster response likely evolve as the technology improves?
Artificial intelligence, or machine learning, brings a host of benefits to business continuity and disaster response, including augmented intelligence, in which humans and machines can collaborate to upskill employees. Learning by human-in-the-loop suggests ways to collaborate efficiently between humans and machines, as the response evolves.
As enterprise disaster response models evolve, they would focus more on selecting salient human processes for feedback, controlling the quality of human process annotations, and designing annotation interfaces. The business continuity response will mature in creating and selecting better process training data for labelling, object identification, semantic segmentation, triage, and labelling. To properly evaluate and train one's system, it is imperative to identify appropriate data and, as the system evolves, to select annotation quality control methods. Interfaces can vary depending on the business since you design them to optimise accuracy and efficiency. With technological improvements, such as the advent of transfer learning and self-supervision, the process mining techniques would mature and become more powerful, reaching human parity.
What are the AI's disaster recovery drawbacks, if any?
Artificial intelligence-driven disaster recovery and business continuity responses are challenged by faulty correlations and false positives. Adaptive learning cannot be fully automated due to a lack of sufficient and pertinent training data, latent events, anomalies, and outliers. This necessitates human intervention and continual feedback to keep processes on track.
Is there anything else you would like to add?
Business continuity, disaster recovery, and incident response will certainly look different in the future thanks to AI - providing it protects a company's mission and resources by adding value. AI and cognitive computing being ubiquitous, individual disaster response products provide useful systemic recommendations in specific domains. Several companies with business continuity or disaster recovery offerings are already considering whether AI would be beneficial to their services. Other providers which use AI-based systems would be able to gain a competitive edge and enhance their business continuity and disaster recovery planning processes more effectively.