The goal of AI security compliance is to ensure that AI systems process data securely and ethically in line with external regulatory requirements and internal policies. This involves deploying technical security controls, monitoring AI activity, and keeping records that demonstrate compliance.
Contents:
Key takeaways:
AI security compliance is the practice of ensuring AI systems handle data securely and ethically, according to internal AI policies and regulatory requirements, such as the EU AI Act or the GDPR.
Some AI-related regulations are mandatory, such as the EU AI Act. Others, like the NIST AI RMF, are voluntary standards that help businesses manage AI risks.
The most common AI security compliance risks are shadow AI, exposed API keys, uncontrolled AI access, and missing audit records.
To address these AI-specific risks, organizations need additional security measures, such as strict access controls and AI system monitoring. Traditional controls still apply, but they need to be adapted to AI-specific threats.
Secure access to AI systems helps businesses meet compliance requirements. Credential managers like NordPass help protect credentials used to access AI tools and reduce the risk of unauthorized access.
What AI security compliance covers
AI security compliance applies to organizations that build AI tools, integrate them through APIs, or use third-party AI models.
It covers 3 areas:
AI systems. AI apps, embedded AI tools, autonomous agents, APIs, and autonomous agents your teams use, and the infrastructure that supports them.
Data used in AI. The data used to train, fine-tune, and run AI systems, as well as prompts, retrieval sources, and outputs.
Operations. Access controls, vendor security checks, activity monitoring, and incident response procedures.
To demonstrate compliance, organizations need documented policies, security controls, test results, and audit logs that show how they meet applicable requirements.
These requirements include 4 key elements:
| Element | What it means |
|---|---|
| Data protection and privacy | AI systems that handle personal and sensitive data must meet requirements under regulations such as HIPAA and GDPR, as well as the relevant obligations under the EU AI Act and PCI DSS. |
| AI model security and integrity | Tracking and controlling data access in AI systems is challenging. Protecting AI from data theft, manipulation, and attacks like prompt injections is essential. |
| Model transparency and explainability | AI systems can be difficult to interpret. The EU AI Act sets specific transparency requirements and grants individuals affected by certain decisions involving high-risk AI systems the right to an explanation. |
| Fairness and algorithmic bias in AI | Models learn discrimination from flawed historical data, which can produce discriminatory outcomes. Organizations need to address these risks. |
These elements also overlap with responsible AI, which guides how organizations develop, deploy, and use AI systems in a safe, ethical, and transparent manner.
Key regulations and standards that matter for AI security
According to ISACA’s 2026 research, 26% of surveyed European organizations didn’t use an AI governance framework, and only 12% reported having a documented, regularly tested process to shut down or override AI systems.
These gaps can create regulatory risks, especially as governments introduce AI-specific requirements. For example, certain violations of the EU AI Act can lead to fines of up to €35 million.
Here are key regulations and standards that organizations need to understand when managing regulatory compliance for AI.
1. EU AI Act
The EU AI Act is the European Union’s AI law. It is mandatory for providers and deployers inside and outside the EU, whose AI systems are placed on or used in the EU market.
It categorizes AI systems into 4 risk-based tiers:
Unacceptable risk (banned). Article 5 bans the following practices: Exploitation of vulnerabilities
Most social scoring
Harmful manipulation
Untargeted facial recognition scraping
Emotion recognition in workplaces and educational settings
High risk (strictly regulated). Certain AI systems used in critical infrastructure, employment, biometric identification, law enforcement, and education require data governance, technical documentation, and human oversight.
Limited risk (transparency obligations). Chatbots and deepfakes have to disclose to users that they are interacting with AI.
Minimal risk (largely unregulated). Most AI tools and applications are not subject to the law’s strictest requirements, although other data protection laws still apply.
2. NIST AI Risk Management Framework (AI RMF)
On January 26, 2023, the US National Institute of Standards and Technology (NIST) published the AI RMF. This voluntary framework applies to organizations across all sectors, both in the United States and internationally. It helps organizations assess, identify, and manage risks throughout the AI lifecycle.
The AI RMF doesn’t exactly specify how to build an AI management system. Instead, it focuses on the attributes of trustworthy AI through 4 functions:
Govern. Promotes a security-aware culture, defines legal and internal policies, assigns responsibilities, and addresses third-party risks.
Map. Identifies and categorizes AI systems and their potential benefits, impacts, and risks to individuals, organizations, and society.
Measure. Assesses the trustworthiness and risks of AI systems and collects feedback using quantitative and qualitative methods.
Manage. Focuses on identified risks, responds to them, and plans mitigation strategies.
The NIST Generative AI Profile is based on the AI RMF and provides additional guidance on GenAI-specific risks, such as inaccurate or fabricated outputs (confabulation), data leakage, and content provenance.
3. ISO/IEC 42001:2023
ISO/IEC 42001:2023 is an international standard for artificial intelligence management systems (AIMS). Organizations can get certification by demonstrating that their AI management system meets its requirements.
Like ISO 27001, ISO/IEC 42001:2023 follows the plan-do-check-act (PDCA) methodology. Organizations must establish AI management processes, monitor how well they work, and regularly review them.
The standard sets 38 specific controls across 9 domains that organizations must evaluate and either implement or formally justify excluding.
ISO/IEC 42001:2023 certification provides independent assurance that an organization’s AI management system meets the standard’s requirements within its defined scope. ISO/IEC 42001:2023 addresses AI governance—how you manage AI systems, not just how you secure them. That distinguishes it from SOC 2, which provides a different form of assurance about in-scope service organization controls.
4. GDPR, CCPA, HIPAA, and SOC 2
AI systems that process personal data—and most of them do—fall under the GDPR, CCPA, HIPAA, or SOC 2 trust criteria. Across all 4 frameworks, the requirement is the same: know what personal data enters your AI systems, control where it goes, and prove it.
Here is how each framework applies AI data security:
| Security regulation | Who it applies to | What changes with AI |
|---|---|---|
| General Data Protection Regulation (GDPR) | Organizations that process personal data of EU residents | Processing personal data with AI may create risks to individuals’ rights. A Data Protection Impact Assessment (DPIA) is required before processing if the processing is likely to result in a high risk. |
| California Consumer Privacy Act (CCPA/CPRA) | Organizations that process personal information of consumers in California | The focus is on opt-out rights and automated profiling. Consumers have the right to refuse having their data used for automated decision-making or training commercial AI systems. |
| Health Insurance Portability and Accountability Act (HIPAA) | US covered entities and business associates that handle Protected Health Information (PHI) | A HIPAA-compliant Business Associate Agreement, along with appropriate safeguards, may be required if a third-party AI provider creates, receives, maintains, or transmits ePHI on behalf of a HIPAA covered entity or business associate. Before sharing PHI, healthcare organizations should assess the provider’s permitted uses, data retention, access controls, and security arrangements. |
| SOC 2 Type 2 | Service organizations that handle customer data | The CPA examines the design and operating effectiveness of in-scope controls over a specified period. Where AI services are in scope, the relevant controls may address access, data handling, confidentiality, and the organization’s commitments about the use of customer prompts. |
AI friction across the 4 frameworks
Training vs. data minimization. The GDPR mandates that only the minimum necessary data be collected for a specific purpose. LLMs do the opposite—consume large datasets indiscriminately.
“The right to be forgotten” vs. deep learning. Individuals can demand deletion of their data according to GDPR and CCPA. However, a trained model can’t “unlearn” personal information once it has been absorbed. Deleting the influence of one person’s data on billions of parameters is impossible.
Data leakage via prompts. When employees enter PII or medical symptoms into public AI tools, that data may be logged, retained, or used for training. For a hospital, that is a HIPAA breach. For a SaaS vendor, it violates SOC 2 confidentiality commitments.
Automated decision-making. GDPR Article 22 restricts certain decisions based solely on automated processing where they have legal or similarly important effects on individuals. Organizations that use AI for decisions, such as credit or employment screening, should assess whether Article 22 applies to them and implement the required safeguards for their use case, which may include human oversight.
Key AI security compliance risks
Traditional security and compliance controls apply to AI systems. But AI systems need a different approach as they learn from new data and work on statistical probability and patterns. An AI system that is compliant today may not be tomorrow.
These 4 areas need particular attention when managing AI compliance risks:
1. Shadow AI
Shadow AI refers to all the AI tools employees may use without IT approval or oversight. According to ISACA’s 2026 research, 33% of surveyed organizations don’t require employees to disclose their AI use, which makes it harder to track where company information goes.
As a result, sensitive company data often ends up in third-party AI systems without the security team’s knowledge. Organizations also struggle to find out what data was shared, where it was stored, and whether it can be deleted.
2. Exposed API keys and credentials
Developers may accidentally expose API keys by storing them in source code, sharing them in messages, or failing to protect them properly. Attackers who intercept these credentials can get unauthorized access to AI models, connected databases, or sensitive company information. GitHub’s 2026 research found that manually revoking an exposed credential takes about 40 days on average, leaving API keys and other secrets usable long after their exposure.
3. Uncontrolled access to AI systems
Without role-based access control (RBAC), employees may access AI models and data they don’t need for their jobs. Stolen credentials can also let attackers reach these systems, extract sensitive information, or perform unauthorized actions.
According to IBM’s 2026 Cost of a Data Breach Report, more than 20% of surveyed organizations experienced a breach that targeted their AI models or applications. Compromised APIs, applications, or plug-ins contributed to 27% of those breaches.
4. Missing logs and audit records
Organizations need records of AI system activity to investigate incidents and demonstrate compliance. Without sufficient logs of access, administrative changes, and relevant AI interactions, security teams can’t trace how sensitive data was used or shared.
The missing evidence also makes it harder to answer regulatory inquiries, complete customer security assessments, and pass audits.
How to achieve AI security compliance
Gain visibility into your data across all AI tools
Effective AI governance starts with an inventory of all AI assets. This gives security teams a view of how data flows through AI.
The following steps provide a practical starting point:
1. Catalog all your AI tools:
AI agents and assistants, such as ChatGPT, Claude, and Copilot
AI apps like chatbots, embedded AI features used across your business, and RAG systems
Databases and data stores, such as vector databases, traditional databases, and data lakes
All organizational endpoints
2. Classify exposure risks for:
Shadow AI tools. Use of unsanctioned AI in your organization.
Sensitive data oversharing in AI tools. When employees share sensitive company data in unapproved AI models.
Violations of security policies. Data usage that doesn’t comply with your company’s policies.
3. Investigate risks and trace data through the AI lifecycle
When you detect a potential data exposure, reconstruct how the data moved through AI systems to understand what happened and who was affected:
Check where the data came from and what it contained.
Verify how the data was processed and modified as it moved through AI.
Track where AI-generated outputs go, and who accessed or shared them.
Investigate what data was exposed and what systems were affected.
Document your findings. They help your security team investigate future incidents and provide evidence during AI compliance audits.
Enforce strict access governance and zero-trust secrets
This is the core element of AI access control, and it splits into 2 pillars:
Access governance: controlling what users and models can see
Restrict entry by identity, not location. Add MFA and centralized SSO to AI model access.
Apply context-aware authorization. Check the user’s permissions before your vector database fetches context for a prompt. If someone doesn’t have access to a folder, your search engine must omit those files from the AI’s context window entirely.
Mask data at the proxy layer. Place a proxy between users and external AI vendors that automatically scrubs PII, credit card numbers, and health data before a prompt leaves your environment.
Zero-trust secrets: controlling how systems authenticate
Centralize your secrets. Store all API keys and service tokens in a secrets manager.
Use short-lived tokens. Replace permanent API keys with credentials that expire within minutes.
Rotate and revoke access automatically. Schedule rotation for keys connecting to your vector databases, LLM providers, and data lakes. Revoke access when someone leaves.
Limit AI agent permissions. An agent that books flights or updates CRM entries should only have API access to that single task, not to the entire enterprise system.
Implement monitoring, logging, and data safeguards
Once access controls are in place, monitor AI activity and keep records that help detect security issues and demonstrate compliance.
This includes logging user access, administrative changes, model updates, and relevant security events. Organizations should also apply appropriate data masking and retention controls to protect sensitive information.
Several AI governance frameworks provide guidance on what to monitor and assess:
CISA recommends monitoring the model itself. Watch for prompt injection, data poisoning, and extraction attempts—attacks that bypass network-level defenses.
NIST AI RMF suggests measuring model behavior. Test your models for accuracy changes, bias, and misuse before deployment and on an ongoing basis.
ISO 42001 provides requirements for managing and reviewing AI risks through a documented management system, internal audits, and regular assessments.
How NordPass supports AI security compliance
The AI tools used by your organization multiply the number of credentials, including employee passwords and API keys. Weak, reused, or exposed credentials can give unauthorized users access to AI platforms and connected business systems.
NordPass helps organizations protect these credentials and support access-related compliance controls through the following features:
Granular role-based access control. Assign permissions by role and team, so only the right people can reach the credentials for AI platforms, APIs, and pipelines.
Single sign-on (SSO) integration. Connect NordPass to your identity provider for centralized authentication.
Where supported and configured, user provisioning integrations can help manage access to NordPass as employees join, change roles, or leave.
Access to the underlying AI services must be managed separately.
Activity logs. Review records of credential-related activity to support security investigations and internal audits.
Compliance auditing. Use built-in reporting to support internal reviews and external audits.
Data Breach Scanner monitors the dark web and public data dumps 24/7 to verify if your sensitive information was exposed.
Get instant breach alerts about compromised credentials, API keys, and credit card details.
Receive step-by-step instructions on what to remediate, including generating new passwords with the built-in Password Generator.
Want to know more about how NordPass protects your access to AI tools? Contact our team.