What is AI Compliance?

AI is quickly becoming a key component of modern operations. At the same time, it’s creating risks of its own. AI compliance and security challenges are hitting the desks of CISOs everywhere as adoption and innovation are moving faster than legislation at industry, state, federal, and regional levels.  

Maintaining compliance will become even more important as regulators catch up with advances in AI technologies. While specific laws and compliance standards may take some time, AI technologies must still comply with privacy, security, and industry-specific regulations. 

This post will cover the core elements of AI compliance, common challenges, best practices for modern enterprises, and how AI Security Posture Management (AI-SPM) can help reduce compliance risk. 

Core elements of AI compliance

The dynamic “black box” nature of AI systems creates challenges for organizations and regulators as they try to apply policies to AI technologies. The sections below break down four core elements of modern AI compliance. 

1. Data privacy regulations

Data privacy is a critical and complicated component of AI compliance. AI systems processing personal and sensitive data must follow regulations that govern the collection, storage, and processing of that data. These regulations include:

• General Data Protection Regulation (GDPR)

• Health Insurance Portability and Accountability Act (HIPAA)

• Payment Card Industry Data Security Standard (PCI DSS)

Ensuring data privacy becomes more complex when handling data unique to AI systems. Because of the frequency at which AI systems interact with new data and the complexity of the datasets, having a purpose-built monitoring tool like an AI-SPM is integral to meeting necessary protocols and regulations.

2. Data security and integrity

Securing data means utilizing good encryption, access controls, and safeguards to protect against attackers who might misuse or tamper with the data.

AI systems are constantly updated, and tracking and controlling data access can be difficult. This is because AI systems continually have new training data, creating new models and outputs.

AI systems can refer to real-time data with tools like retrieval-augmented generation (RAG), which allows an AI model to refer to data it was not trained on, like internal documentation in a vector database.

3. Model transparency and explainability

AI systems are unique in that they may need to explain why they made a specific inference. Yet, AI models lack transparency, even to the professionals working directly with them. 

Research on explainability for AI models indicates inversely proportional performance when explainability is increased. AI-SPM helps measure and inventory the performance of a model in the event that improving explainability inadvertently reduces model performance.

4. Bias and fairness in AI

Algorithmic bias and fairness were already controversial tech topics, and AI has supercharged the scrutiny. Achieving compliance for areas like finance, healthcare, and HR hinges on proving AI models aren’t exhibiting bias in the form of illegal discrimination.

However, the way AI models' interact with data complicates this dynamic, as human biases mixed with incomplete data can amplify existing biases.

Common challenges in maintaining AI compliance

AI systems are unique because they process large quantities of (possibly sensitive) data and allow users to interact with the data differently, resulting in unique compliance and security issues unique to AI systems. Let’s look at three of the biggest challenges facing organizations attempting to achieve AI compliance.

Challenge #1: Dynamic and evolving models

The constantly changing nature of AI systems makes it difficult to follow compliance rules. Retraining and new inference mechanisms makes maintaining compliance a constant challenge. 

Changing data, algorithms, or hyperparameters leads to unpredictable outcomes, endangering a system's compliance. For example, tweaking a model training algorithm may inadvertently introduce a bias that violates equal opportunity laws. 

This dynamic and evolving nature requires monitoring and alerting when something changes within the models that could create compliance risk.

Challenge #2: Data provenance and governance

Data provenance, or where the data used to train an AI model originates, can be a significant compliance headache. AI systems trained on data derived from other model outputs or aggregated from multiple sources are complex and difficult to disentangle and ensure compliance with regulations and ethics guidelines. This is why managing data pipelines is so essential to safeguard compliance.

Challenge #3: Automated decision-making

The last AI compliance challenge relates to the automated nature of AI decision-making. With autonomous AI systems, determining compliance is difficult because the only verification is a set of software checks, as no human operator is present for real-time intervention.

Four best practices for ensuring AI compliance

To stay compliant with evolving AI regulations, adhere to the following best practices:

Best practice #1: Adopt an AI governance framework

The foundation for AI compliance is a strong AI governance framework. This encompasses policies, procedures, and ethical guidelines your organization employs for AI systems. An AI ethics committee or task force should spearhead initiatives to develop and enforce the governance framework.

Best practice #2: Implement detailed tracking and audit trails

Detailed audit trails can help with rapidly changing systems and data provenance. Organizations should also implement tracking and logging mechanisms for the AI systems to record when models are trained, retrained, and produce responses.

Best practice #3: Enforce data anonymization and minimization

Since privacy is a cornerstone of data compliance, anonymization and minimization are essential for an AI data processing strategy. This aligns with GDPR and similar data privacy and protection regulations.

Best practice #4: Use bias monitoring and mitigation

It’s important to monitor and mitigate possible biases, as failing to do so could create compliance or liability risks. Training models using tools like differential privacy and adversarial testing also mitigate bias and use prompt engineering to prevent prompts from producing biased outputs.

The role of AI-SPM in AI compliance

AI Security Posture Management (AI-SPM) provides a strategic framework that covers regulatory and security concerns for your AI systems. AI-SPM solutions manage and secure AI models throughout their lifecycle and defend models from exposure risks or data poisoning attacks.  

AI-SPM offers continuous adherence to security standards and regulatory requirements and documents compliance with GDPR and other national and regional laws.

The table below details the essential components of AI-SPM tooling.

Data protection and shadow AI detection

Modern data loss prevention (DLP) and data protection tooling help round out AI-SPM and provide organizations with a strong line of defense against sensitive data exposure. Data protection tooling such as CrowdStrike Falcon Data Protection goes a step further and can proactively detect unauthorized “shadow” generative AI tooling. 

How CrowdStrike helps you achieve AI compliance

The use of AI systems comes with additional complexity in complying with laws and regulations that governments have created and are still in the process of creating. Data privacy regulations, data security, integrity provisions, model explainability, and biases are a core part of AI compliance and are core elements that must be addressed. 

AI-SPM can be an important tool to document your compliance — as part of an AI governance framework — in handling AI’s dynamism, need for large datasets, and autonomous nature.

CrowdStrike’s AI-SPM provides a real-time monitoring solution for your AI systems, protecting data and AI models, all while enabling regulatory compliance. The AI-SPM offers organizations: 

• Visibility across their AI systems
• Tracking compliance and gap identification
• Reduction in the risks of legal trouble
• Detection of potential security threats before they escalate

Additionally, CrowdStrike Falcon Data Protection complements AI-SPM by detecting unauthorized generative AI tools and implementing security controls on egress data that reduce the risk of sensitive data exposure. 

As a trusted cybersecurity partner, CrowdStrike maintains multiple compliance certifications to help organizations leverage industry-leading tooling while meeting audit requirements. To see how CrowdStrike’s AI-SPM can help your organization, start your free trial today. Alternatively, you can try out the interactive demo.