6 Key Components of a Robust AI Compliance Strategy

AI has become a core part of how businesses build and ship software. In fact, 92% of organizations say their developers are using GenAI tools to generate application code. However, as adoption expands beyond IT and developers, organizations have to begin thinking more critically about AI’s implications for security and compliance. 

AI attacks and threats are becoming more common, exposing unprepared businesses to more risk. Whether organizations use an established provider or model, such as OpenAI and ChatGPT, or develop their own, having a robust AI compliance strategy and security posture is essential for future-proofing your business. 

With the right AI compliance strategy, companies can add an extra layer of security to their systems, reducing the impact of an AI attack or breach and preventing noncompliance with existing rules and regulations. 

What is an AI compliance strategy? 

An AI compliance strategy ensures that the AI systems and tools companies use comply with local laws, regulations, and ethics. It also includes how companies use AI internally or when developing AI systems, like Large Language Models (LLMs). 

Because AI introduces new risks across people, processes, and technology, compliance isn’t one-size-fits-all. A well-rounded strategy includes clear policies, thoughtful risk management practices, and modern tooling, all working together to strengthen security.

Importance of AI compliance strategies 

AI compliance strategies help protect companies from risk, as AI tools and systems are inherently insecure and constantly evolving. Many users are not fully aware of the security issues that AI can introduce or how new systems and tools pose risks to their company. Without a dedicated strategy or plan, employees and users could unknowingly expose products or companies to vulnerabilities and other security issues. This can lead to the exploitation of AI systems and tools by bad actors. 

Similarly, if development teams use AI coding tools without additional security guardrails, they can introduce vulnerabilities across codebases and products. AI compliance helps avoid these issues while protecting companies from significant financial losses or damages. 

AI compliance is also important to:

• Build trust with customers, partners, and key stakeholders. 

• Foster a culture of transparency and accountability surrounding AI usage and development.

• Protect sensitive data and personal information, and follow data privacy laws.

• Easily stay up to date with evolving regulations and proactively address concerns to avoid impacting growth or scalability. 

Regulations surrounding AI compliance 

While the U.S. doesn’t have specific laws surrounding AI systems and tools yet, they are being developed at both the state and federal levels. There are also voluntary frameworks, like NIST’s AI Risk Management Framework, that help companies manage risk when using or developing AI.  

That said, AI systems and tools are part of a company’s IT ecosystem and are often used in the software development lifecycle (SDLC), which can fall under state and federal compliance frameworks and regulations. This is especially true for companies operating in highly regulated industries, including financial services, healthcare, or insurance. 

As these industries have strict data governance and cybersecurity laws, such as the Health Insurance Portability and Accountability Act (HIPAA) and the California Consumer Privacy Act (CCPA), AI systems must be designed and used with privacy, security, and safety in mind. 

Additionally, if companies operate globally or plan to expand operations, there are AI laws in other countries. For example, the EU has the AI Act, which regulates the development and use of AI. With the AI Act, companies must comply with strict requirements before entering the market, including risk assessment and mitigation systems, as well as detailed documentation. 

6 key components of an AI compliance strategy 

Developing an AI compliance strategy doesn’t have to be overwhelming. While there are many moving parts as companies work through policies and procedures, breaking a strategy down into six key areas can make it easier to manage.  

1. Risk management   

An integral part of any cybersecurity or compliance strategy is risk management. By actively managing risk, companies can minimize the impact of a security event while ensuring their AI systems are secure. Companies should:

• Regularly conduct risk assessments: Review AI systems regularly to identify potential risks and vulnerabilities. This includes categorizing, prioritizing, and analyzing risk to determine criticality and next steps.  

• Implement risk mitigation procedures: Companies must develop and implement controls that address critical risks in AI systems. Common risk mitigation procedures include process changes, additional security policies or measures, and human oversight. 

• Use continuous monitoring and security tooling: Continuous monitoring tools, such as a security and event management (SIEM) system, can help detect vulnerabilities, security threats, or compliance issues with AI systems and broader IT infrastructure.  

• Develop an incident response plan: Having an incident response plan is often required by compliance frameworks and regulations. In the event of a security incident, key stakeholders and teams should know what actions to take to minimize the incident's impact.   

2. Data privacy

AI is built on data, so sensitive or private data needs to be protected. Not only is it necessary for highly regulated industries, but it also ensures that a company doesn’t face breaches or other critical security events. 

• Follow data privacy laws and regulations: Organizations should be aware of any rules or regulations surrounding data privacy in their industry or region. When handling data in AI systems, they should comply with data privacy regulations. This can include implementing data minimization to reduce the amount of information AI systems process or collect. 

• Have strong data security measures: Traditional data security measures weren’t developed for AI systems. In many cases, businesses need to adopt a multi-layered approach to ensure AI data security and privacy. Using encryption, access controls, least privilege principles, and security audits can protect data used in AI systems from unauthorized access or exploitation. 

3. Governance

Along with data privacy, businesses should have a strong governance framework. This will ensure data security and compliance with regulations while also providing guidance and direction for the proper and ethical use of AI. 

• Define ethical boundaries: Organizations should define what responsible AI usage looks like. As ethical boundaries are defined, companies can then use them to inform internal policies and procedures surrounding AI. 

• Develop internal policies, procedures, and documentation: These should cover acceptable use of AI and data handling. Businesses should also create an AI Bill of Materials (AIBOM) that lists every aspect of their AI systems, including data pipelines, training procedures, and operational performance.

• Comply with existing governance frameworks: Even if your organization isn’t required to comply with a specific framework, adopting one can be helpful. ISO 42001 is an international standard with specific requirements for establishing, using, maintaining, and improving the AI systems you use or create. 

• Create an oversight committee: Human oversight is key when using AI systems. Having people regularly review processes, projects, logs, and systems will reduce risk and make sure systems maintain compliance. 

4. Data quality and bias 

AI can hallucinate, and bad actors can carry out data poisoning or other types of prompt injections, which could impact the quality of data, inputs, and outputs. Having guidelines that set ethical standards for AI systems to work accurately and consistently can look like:

• Consistently reviewing and monitoring data quality. AI runs on data, so quality counts. Reviewing data quality can prevent inaccuracies and feedback loops. It can also help identify any bias that exists in the training data, which could lead to biased or unfair outputs. 

• Implementing bias mitigation techniques. Apart from reviewing data for bias, companies can implement data reprocessing or bias detection tools to ensure they avoid patterns of bias in AI systems. 

5. Training and awareness

AI is still a relatively new technology, so organizations can’t expect all users to be familiar with it. Providing training and awareness to employees on AI systems can increase adoption and ensure compliance. It’s also a great way to add an additional layer of security to your strategy. If employees know the signs of a malfunctioning system or security issue and the steps to take, it can reduce the impact. 

Training also promotes a culture of transparency, accountability, and responsible AI usage. This makes implementing compliance procedures, ethical boundaries, and internal policies much easier. 

6. Monitor compliance and regulations 

It’s essential to stay informed as you develop your AI compliance strategy. Compliance regulations and requirements will continue to change as more businesses adopt and develop AI systems. Knowing what’s happening and how it can impact your business will make transitions easier. It’s also a great way to keep your AI compliance strategy up-to-date instead of having to overhaul it when you enter a new market or laws are enacted. 

How to implement an AI compliance strategy

Every organization’s approach will be a bit different, depending on where and how you use AI. That said, here are a few high-level steps you can use to get started:

1. Introduce the compliance strategy: Make sure everyone knows their role, from leadership to engineering, to legal and compliance.  

2. Set clear goals, prioritize implementation, and establish timelines: Start with the highest-risk areas and define when policies and training will roll out. For more urgent, high-risk items, such as a required compliance framework, this may be immediately or within a shorter timeframe. 

3. Implement additional security and risk management procedures: A robust security posture is one of the most important parts of an AI compliance strategy. Adding security tools specifically for AI and developing AI-specific incident response plans can address AI security issues that could impact compliance. 

4. Begin or continue training programs: All employees or users of AI systems should complete training or awareness programs. Programs typically cover responsible use, company guidelines, ethical standards, and data security and privacy.  

5. Provide feedback for continuous improvement: Teams should feel comfortable sharing feedback on how the implementation is going. This can identify where the strategy is falling short or any challenges with implementation. 

6. Regularly review and update the AI compliance strategy: Consider quarterly reviews to ensure the strategy is still relevant and effective. Keeping the strategy up to date, outside of changes to regulations or requirements, will make it easier to maintain over time.

How Snyk elevates your AI compliance strategy

To have a robust AI compliance strategy and implement best practices, a business needs the right tooling. Snyk, an AI-powered developer security platform, makes it easy to integrate security into every step of your SDLC, including AI systems and tools. Not only can Snyk help you safely adopt AI, but it can also support your compliance goals, streamlining processes to ensure you meet global security standards.

Snyk makes it easy for your DevOps team to leverage generative AI that boosts productivity while keeping your SDLC secure. With Snyk Code, powered by DeepCode AI, developers can find, fix, and prioritize vulnerabilities in code without switching tools or context, ensuring that both AI-generated and human-generated code is secure. Snyk Code’s real-time scanning and auto-fixing reduce companies’ risk, putting security earlier in the product lifecycle, making it easier to comply with frameworks, regulations, and policies. 

To learn more about Snyk’s capabilities and how its developer-first features can make compliance and AI adoption seamless, book a demo today.