Building an AI Incident Response Framework
The recent Crowdstrike outage serves as a stark reminder of how vulnerable our increasingly interconnected digital infrastructure has become. A faulty software update in Crowdstrike’s product for Microsoft Windows users led to widespread disruptions for airlines, banks, retailers and other businesses globally. While the company emphasized it was not a security incident or cyberattack, but rather a technical glitch, the impact was no less severe.
This incident highlights an uncomfortable truth that applies equally to artificial intelligence systems – no matter how much testing and preparation is done, things will inevitably go wrong. As AI becomes more deeply embedded in critical business processes and customer-facing applications, organizations must prepare for AI incidents with the same rigor they apply to cybersecurity incidents.
AI incidents are inevitable; the question is not if, but when, and how well organizations are prepared to respond.
I recently explored this critical topic in depth during a podcast conversation with Andrew Burt, co-founder of Luminos.Law and Luminos.AI. These entities are building tools to help companies mitigate and manage AI risks. Our discussion highlighted the unique challenges of AI incident response compared to traditional software incidents, and why existing processes don’t directly translate.
The Unique Nature of AI Incidents
AI incidents stem from the probabilistic, often unintended behaviors of machine learning models. Unlike cybersecurity incidents which typically involve malicious actors, AI incidents arise from the inherent uncertainties and limitations of the technology itself. A model may produce biased outputs, violate privacy, infringe on copyrights, or generate harmful hallucinations – all without any malicious intent.
AI incidents, unlike cybersecurity breaches, often stem from the inherent uncertainties and limitations of AI itself, not malicious actors
This fundamental difference means that existing incident response processes focused on finding and ejecting bad actors don’t apply. Organizations need a new playbook specifically tailored for AI incidents.
Defining and Detecting AI Incidents
The first crucial step is clearly defining what constitutes an AI incident for your specific use case and risk profile. This isn’t as straightforward as it may seem. While severe issues like racist outputs or major privacy violations are obvious incidents, more subtle problems like slight biases or occasional hallucinations may fall into a gray area.
Organizations need to carefully consider their risk tolerance and define incident thresholds across dimensions like accuracy, fairness, privacy, and safety. These definitions should be codified in policies and procedures accessible to all relevant teams.
Once incidents are defined, detecting them becomes the next challenge. Traditional software incidents often trigger automated alerts, but AI incidents are trickier to spot. Many companies rely heavily on social media monitoring to identify issues – a risky approach that means problems may go viral before the company is even aware.
More robust detection requires going beyond simple accuracy tracking. Organizations should implement comprehensive monitoring across multiple dimensions, potentially leveraging AI itself to detect anomalies and undesired behaviors. Importantly, this monitoring needs to extend to non-accuracy related issues that may have legal or reputational impacts.
The Phases of AI Incident Response
While the specific actions differ, according to Andrew Burt the high-level phases of AI incident response mirror those of traditional incident response:
- Preparation: This critical phase involves defining policies, procedures, roles and responsibilities. Organizations should establish internal response teams spanning data science, legal, PR, and other relevant functions. External experts should also be identified and engaged as needed.
- Identification: Detecting that an AI incident has occurred, leveraging the monitoring capabilities established during preparation.
- Containment: Taking short-term actions to limit and mitigate the impact of the incident. This phase is especially critical for AI incidents, as investigation and remediation can take significant time.
- Eradication: Investigating the root cause of the incident and addressing the underlying issues in the AI system.
- Recovery: Restoring system functionality and performance, potentially with additional safeguards in place.
- Lessons Learned: Reviewing the incident and response to improve preparedness for future incidents.
The Importance of Containment Plans
Andrew Burt aptly stresses the critical importance of establishing predefined containment protocols to effectively address potential AI incidents. The effectiveness of an AI incident response often hinges on whether short-term containment options were prepared in advance, potentially making the difference between disaster and success.
AI systems are complex and brittle. When something goes wrong, it can take weeks or even months to fully diagnose the issue and develop a fix. If the incident is severe, waiting that long to take action is simply not an option.
Pre-emptively establishing short-term containment options for AI incidents can mean the difference between disaster and successful mitigation
Effective containment plans may include options like temporarily changing model behavior, reverting to backup versions, or even disabling certain AI functionalities. While these actions may impact business operations, they can prevent much more severe reputational or legal consequences.
Having these plans ready allows organizations to act decisively in the critical early hours of an incident, rather than scrambling to develop options under intense time pressure and public scrutiny.
Analysis and Recommendations
Based on the Crowdstrike incident and broader trends in AI deployment, I offer the following recommendations for organizations building and deploying AI systems:
- Address Supply Chain Vulnerabilities and Single Points of Failure. AI applications often rely on complex software stacks and third-party libraries, making them susceptible to supply chain vulnerabilities. A weakness in any component, whether it’s a library, tool, or service, can compromise the entire system. Additionally, reliance on single vendors for critical infrastructure components creates significant risks. A vulnerability or issue in their product can simultaneously affect numerous organizations, underscoring the fragility of modern IT infrastructure.
- Focus on real security, not security theater. Don’t rely solely on compliance checklists or off-the-shelf security products. Build security into the fundamental design and architecture of AI systems.
- Manage complexity. The intricacy of modern AI stacks creates numerous potential vulnerabilities. Strive for simplicity where possible and ensure a deep understanding of all components.
- Be cautious with automatic updates. Uncontrolled updates can disrupt carefully tuned AI systems. Implement staged rollouts and robust testing processes for all changes.
- Go beyond compliance. While regulatory compliance is important, it is insufficient for truly securing AI systems. Implement practical, effective security measures that address your specific risks.
- Implement staged rollouts. Gradually deploy updates and new models to detect issues before widespread impact.
- Develop a unified AI Alignment Platform. Instead of relying on disparate tools that only address individual AI risks, adopt a unified AI alignment platform. This platform should offer workflow management to streamline collaboration among teams, provide analysis and validation tools to quantify risks like bias and privacy violations, and maintain comprehensive reporting for demonstrating due diligence and ensuring accountability as you scale your AI initiatives.
AI incidents are inevitable. The question is not if they will occur, but when, and how prepared organizations will be to respond. By developing robust incident response capabilities tailored to the unique challenges of AI, companies can minimize the impact of incidents and build trust with customers, regulators, and the public.
Related Content
If you enjoyed this post please support our work by encouraging your friends and colleagues to subscribe to our newsletter: