Guide for Incidence Response Planning for AI

With the rapid progress of the Artificial Intelligence, there is a significant chance of failure. There is no technological development without failure, just that the outcome of the failure should not be disastrous. AI failures can cluster based on security, privacy violations, or lack of transparency and accountability. The companies must be prepared to respond to massive failures which may involve legal aid.

AI Incident Databases provide insights into the various incidents that have occurred and the importance of updating the data. AI failures can cause huge financial losses and also hamper the reputation of the company. Here the AI incidence response plans come for rescue. The incident response plans are necessary as AI is different from other traditional software.

The input data on which the AI work is historical data so they need to be constantly updated with time. The complexity of the AI especially systems which involves generative AI or deep learning has many interconnected nodes which makes it difficult to identify where the error has occurred. Minor inaccuracies in the system can lead to massive failures affecting the mankind.

AI incident response plans need to take advanced cybersecurity measures which is beyond the traditional cybersecurity measures that are primarily taken for specific threats such as data breaches.

Here are a few steps that can be followed for AI incident response:

Preparation

• Certain policies and procedures must be followed to address a specific incidence response. The response must define the term and the threat it caused and the roles and responsibilities taken for it.
• Include the event and the error which led to the occurrence of this threat as well as the result from the external attack.
• Frame policies and procedures at involve the failure at all stages of the model or system life cycle.
• Initiate training in the organization to operate and perform the policies to aware people of the incident.

Identification

• Follow the industry standards to detect the incident.
• Monitor the AI platforms to find out the damage that the AI can cause.
• Look for feedback from the consumers or organizations who operate it.

Containment

• Take steps to address the immediate damage, then pause the operations and look for backup to tackle the situation.
• Follow procedural directions to evaluate the incident otherwise it may cause severe damages.
• Try to fix technically as identified by engineers to minimize the harm cause by it.

Eradication

• Remove the system that caused the incident and do not perform any upgradation in the system until it is reviewed and feedback is provided that no further incidence will occur regarding the system in future.
• Perform documented testing in the reviewed or replaced system especially those systems that led to incident.

Recovery

• The revised system should be hardened before deployment.
• Benchmarked the replaced system and output before performing any further development or upgradation.

One of the best way to mitigate damages due to AI is to include interdisciplinary teams in the organization. Risk Managers can help a vital role in coordinating among the technologists and legal professionals that can help us to mitigate such losses.