The NIST AI Risk Management Framework (AI RMF) is tailored for AI companies to manage unique AI-related risks effectively. It focuses on four main functions:
‍
‍Govern: Establish accountability for AI systems.
‍Map: Identify and categorize AI risks.
‍Measure: Evaluate the severity and probability of these risks.
‍Manage: Strategize on mitigating or accepting risks.

This framework helps AI companies integrate risk management into their development process, promoting the creation of reliable and ethically responsible AI applications. The AI RMF Playbook provides actionable steps to implement these principles, making it practical for AI companies to adopt and adapt to their specific needs. This approach enhances both compliance and innovation, building stakeholder trust in AI technologies.

The OWASP Top 10 for Large Language Model (LLM) Applications provides crucial guidelines to secure LLMs against common vulnerabilities. Key areas of focus include:

‍Prompt Injection: Manipulating LLMs through crafted inputs to trigger unintended actions.
‍Insecure Output Handling: Failing to validate LLM outputs, risking security breaches.
‍Training Data Poisoning: Compromising model integrity through tampered training data.
‍Model Denial of Service: Overloading systems with complex inputs, degrading performance.
‍Supply Chain Vulnerabilities: Introducing risks through third-party components.
‍Sensitive Information Disclosure: Inadvertently exposing confidential data.
‍Insecure Plugin Design: Using plugins that process untrusted inputs without adequate controls.
‍Excessive Agency: Allowing LLMs too much autonomy, causing unintended actions.
‍Overreliance: Depending too heavily on LLMs, leading to errors and misinformation.
‍Model Theft: Stealing proprietary models, risking intellectual property and data security.

Addressing these vulnerabilities with thorough testing, robust data governance, and continuous monitoring enhances LLM security and maintains the integrity and trustworthiness of AI systems.

For AI companies undergoing SOC 2 audits, conducting a security audit to test for vulnerabilities is crucial. This includes vulnerability assessments to identify weaknesses, penetration testing to evaluate the defenses against simulated attacks, and risk assessments to understand the potential impacts of these vulnerabilities. These steps are vital for reinforcing the security measures required for SOC 2 compliance, ensuring that the systems can protect sensitive data against security threats effectively.

ISO/IEC 23894:2023 offers tailored guidance for managing risks in AI systems, adapting the principles of ISO 31000:2018 to the unique challenges of AI. It ensures AI applications are robust, secure, and trustworthy. We can assist your organization in effectively integrating these risk management standards throughout the AI lifecycle, enhancing both safety and efficacy​.

Under the EU AI Act, security testing for AI systems, especially high-risk ones, involves ensuring compliance through several key steps:

‍Risk Assessment: Identifying and addressing potential security threats to the AI system.
‍Technical Documentation: Maintaining records that demonstrate the system’s compliance with security requirements.
‍Conformity Assessment: Verifying through formal evaluations that the AI systems meet the Act’s stringent standards before deployment.
‍Continuous Monitoring: Regularly re-evaluating the AI system to manage new and evolving security threats.
‍Data Governance: Ensuring the secure and ethical handling of data used by the AI system.

These measures are aimed at maintaining high standards of security, privacy, and data integrity, enhancing the trustworthiness of AI applications within the EU.