How To Get Started with AI Compliance and Why It Matters

As AI adoption accelerates across industries, enterprises are increasingly pressured to align their technologies with evolving regulatory, ethical, and security standards. Once a secondary concern, AI compliance has become a top priority for organizations deploying generative AI, machine learning systems, and Large Language Models (LLMs) at scale.

In this article, we’ll break down what AI compliance is, why it matters, how to get started, and the frameworks and best practices that can help teams proactively manage risk, security, and trust in AI-powered systems. Whether you’re shipping AI-enabled features, building with open source models, or integrating GenAI into development pipelines, aligning with compliance expectations is no longer optional; it’s foundational.

What is AI compliance?

AI compliance refers to the processes, policies, and technical safeguards that ensure AI systems meet legal, regulatory, ethical, and organizational standards. It encompasses everything from data privacy and security to model transparency, fairness, and accountability.

In modern development, AI compliance means protecting end users from harm or bias and safeguarding organizations from reputational, legal, and financial risk. For example, AI-generated code must be secure and auditable, aligning with frameworks like Snyk’s AI Bill of Materials (AI-BoM) to provide visibility into model behavior, dependencies, and risk exposure.

AI compliance and regulations

Global regulatory bodies are rapidly responding to the risks posed by advanced AI systems. The EU AI Act, General Data Protection Regulation (GDPR), and proposed U.S. frameworks like the AI Bill of Rights aim to create accountability and transparency in AI development. These regulations impose data protection, explainability, human oversight, and misuse prevention requirements.

For developers and companies working across jurisdictions, understanding and complying with these regulations is key to building scalable, trustworthy systems. Failing to comply with regional rules could result in legal action or fines, especially when sensitive data is involved.

Key regulatory bodies and their roles

Several regulatory bodies are shaping the future of AI compliance. The European Commission is leading with the AI Act, classifying AI systems by risk level. The U.S. National Institute of Standards and Technology (NIST) offers the AI Risk Management Framework, while national privacy regulators, like the ICO in the UK or CNIL in France, enforce rules around data protection and user consent.

These entities collectively guide how AI should be assessed, documented, and governed across its lifecycle. Compliance is no longer a checkbox. It requires a cross-functional commitment to transparency, accountability, and secure-by-design AI.

Establishing AI compliance requirements and policies

To meet compliance obligations, organizations need clearly defined AI compliance requirements. These include rules for acceptable data use, consent mechanisms, model auditability, and incident response plans. In development environments, this also means scanning AI-generated code for vulnerabilities and validating model behavior against security and fairness benchmarks.

Compliance policies must be integrated into workflows, from development to deployment, including model documentation, data sourcing declarations, access controls, and formalized risk assessments for AI systems.

Role of compliance programs in AI

A formal AI compliance program provides the structure and accountability required to manage risk effectively. These programs typically include:

• Governance committees for oversight and review.

• Model audit frameworks and testing protocols.

• Processes for handling violations and non-compliance.

• Staff training and awareness initiatives.

By embedding compliance into development and DevSecOps processes, teams can shift from reactive to proactive AI risk management, aligning with modern best practices around secure and responsible GenAI use.

Key elements of an AI governance framework

An effective AI governance framework includes policies, controls, and processes that ensure safe, ethical, and compliant AI development. Key elements include:

• Accountability: Clear ownership of AI systems and decisions.

• Transparency: Documentation of model behavior and logic.

• Risk management: Identification, scoring, and mitigation of AI-specific risks.

• Security controls: Protection against AI-specific attacks such as data poisoning, prompt injection, or model hijacking.

Frameworks like AI-BoM help operationalize governance by making it easier to track dependencies, audit model behavior, and validate outputs against compliance requirements.

AI compliance audits and monitoring

AI systems require regular compliance audits to ensure they meet both internal policies and external regulations. Audits typically include:

• Model performance evaluation across edge cases.

• Bias testing and fairness audits.

• Documentation reviews and transparency checks.

• Security scanning of code, dependencies, and configurations.

Continuous monitoring is also essential. Teams must implement real-time alerting, access logging, and feedback loops to catch drifts, hallucinations, or output anomalies before they become incidents.

Addressing compliance gaps and enforcement actions

When gaps are found, whether through audits, external reviews, or self-reporting, they must be addressed quickly. This includes remediating vulnerabilities, updating model training data, and documenting mitigation steps.

Enforcement actions can range from warnings and remediation orders to fines and bans. By proactively testing and documenting AI systems using tools like Snyk Code, organizations can demonstrate a good-faith approach to compliance and reduce exposure.

Data processing and protection in AI systems

One of the most regulated aspects of AI is data processing and protection. Developers must ensure that training data is lawfully sourced, anonymized where necessary, and used with user consent.

Data privacy laws like GDPR and the California Consumer Privacy Act (CCPA) place strict rules on how personal data can be collected, processed, and stored. AI compliance frameworks must include consent management systems, data retention policies, and secure deletion mechanisms to comply with these standards.

Explainability and transparency in AI compliance

Explainability is a cornerstone of AI compliance. Regulators, customers, and internal teams need to understand how and why a model makes decisions, especially when those decisions have real-world consequences.

Explainable AI (XAI) techniques, such as attention mapping, saliency visualization, Local Interpretable Model-agnostic Explanations (LIME), or Shapley Additive Explanations (SHAP) modeling, help bring visibility to opaque systems. These techniques support not just compliance, but also trust and accountability.

Ethics and fairness in AI

Ethical AI development requires fairness, inclusiveness, and a commitment to minimizing harm. Bias in training data, exclusionary outputs, or discriminatory logic can have serious implications—not just for compliance, but also for reputation and impact.

Tools that evaluate model fairness, benchmark demographic parity, and apply counterfactual testing help ensure that AI systems align with ethical principles. These checks are essential complements to Snyk’s security-first approach to AI, ensuring models are safe and equitable.

AI compliance and cybersecurity

Security and compliance are deeply interconnected. AI systems, especially those with API endpoints or embedded in code pipelines, must be protected from exploitation. This protection includes applying security best practices like dependency scanning, container hardening, and code validation.

Cybersecurity protections must consider AI-specific threats, such as model inversion attacks, output manipulation, or prompt injection vulnerabilities. Integrating security into compliance workflows ensures a unified, scalable defense strategy.

Risk assessments and due diligence

Before deploying any AI system, teams should consider conducting a comprehensive risk assessment, including evaluating data provenance, attack surface, operational impact, and failover protocols.

Due diligence is especially critical when adopting third-party AI models or APIs. Teams must validate vendor claims, scan for open source vulnerabilities, and implement fallback systems in case of service failure or output corruption.

AI compliance training and best practices

Compliance training helps developers, data scientists, and product teams understand their responsibilities. Effective training covers:

• Data privacy and protection.

• Secure model development practices.

• Responsible use of LLMs and GenAI. 

• Security risks of AI-generated code.

Best practices also include maintaining documentation, auditing code, tracking model dependencies, and implementing security reviews for all AI components.

Future directions in AI compliance

As AI systems become more autonomous, compliance requirements will grow more complex. Expect increased emphasis on real-time monitoring, dynamic risk modeling, and cross-border harmonization of standards.

Snyk is helping lead this evolution with developer-first tools that make build secure, compliant AI systems easier, from scanning LLM-generated code to flagging risky dependencies and enforcing policy-as-code controls.

Snyk’s commitment to AI compliance

AI compliance is about more than meeting regulatory minimums. It’s about building trustworthy, transparent, and secure systems that scale with integrity. From securing training data to validating model outputs and documenting code behavior, every step in the AI development lifecycle offers an opportunity to build better, safer technology.

As AI technology continues to evolve, so will the compliance ecosystem. Snyk is committed to staying at the forefront of this evolution, offering developer-first solutions that simplify the process of building secure and compliant AI systems. From scanning LLM-generated code to flagging risky dependencies and enforcing policy-as-code controls, we are here to support your journey towards a safer and more responsible AI future.

At Snyk, we are dedicated to helping developers and security teams stay ahead by integrating compliance and security into every stage of the software lifecycle.