Understanding AI compliance can be complicated due to its rapid development, the constantly evolving risks, and numerous regulations and frameworks to follow. Fortunately, Aim’s platform provides easy monitoring for your security teams, ensuring they effectively manage the increasing risks associated with AI.

Aim addresses compliance obligations through a three-step mitigation approach: discovery, protections, and detections. For each, Aim’s platform facilitates easy tracking of well-known regulations and frameworks, enabling security teams to effectively monitor compliance. This blog post is the second in Aim's lineup of posts clarifying regulations and compliance, providing detailed guidance on how security teams can practically use the platform for this purpose.

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Discovery

Shadow Third Party AI Applications

A key requirement in many regulations and frameworks is to gain clear visibility into AI systems and document their associated risks. Aim provides a comprehensive overview of all third-party AI systems used within your organization, ensuring secure AI use by offering full visibility into content sent to these systems. It includes departmental usage statistics and monitors file uploads to AI applications, illuminating users' AI usage patterns and enabling enterprise-level data-driven decisions.

Examples for supported requirements:

1. NIST
   ‍GOVERN 1.6: Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities
   AI providers or developers must document, track, and allocate resources for AI systems based on risk levels to ensure compliance and accountability.
2. EU AI Act
   Article 12: Record-KeepingAI providers or developers are required to maintain logs generated by high-risk AI systems to ensure an appropriate level of traceability. This obligation ensures that providers keep detailed records of the operation and performance of their high-risk AI systems.

AI-SPM‍

Aim’s AI-SPM capabilities offer comprehensive visibility into your organization's ML and AI flows and assets, including models, datasets, workflows, and endpoints. Additionally, Aim provides risk assessment features like AI red-teaming, code analysis, and model scanning to evaluate your models' posture and resilience against risks and threats such as prompt injections, backdoors, reverse-engineering, bypasses, excessive agent capabilities, excessive model exposure, and data leakage. This ensures robust protection against vulnerabilities and safeguards sensitive information related to your AI models.

Examples for supported requirements:

1. NIST
   MEASURE 2.7: AI system security and resilience – as identified in the MAP function – are evaluated and documented
   AI developers must assess, document, and enhance AI system security and resilience to mitigate risks identified in the mapping process.
2. MITRE ATLAS
   Persistence Backdoor ML Model
   AI developers must detect, prevent, and document hidden backdoors in ML models to prevent unauthorized control and adversarial manipulation.
3. EU AI Act
   Article 15- Accuracy, Robustness, and Cybersecurity
   AI developers must ensure AI systems maintain accuracy, resilience against attacks, and protection from cybersecurity threats throughout their lifecycle.

Protections

Prompt Protections and Auditing

One of the biggest challenges in both internal and third-party AI chats is gaining control over the information flowing in and out of these systems. All frameworks address the significant risk of data leakage involving user, employee, or customer personal information when using third-party AI apps, as it can lead to unauthorized use, disclosure, and de-anonymization of personal data. For internal AI apps, developers and security teams must protect their systems from attacks and compliance breaches. 

Aim conducts constant monitoring of prompts sent to both third-party AI chats and homegrown AI apps. This enables security and compliance teams to regularly monitor AI-generated content for privacy risks and address potential exposure of PII, PCI, PHI, or sensitive data. To further mitigate the risk of linking AI-generated content back to individuals, Aim uses techniques such as anonymization and blocking prompts that conflict with organizational AI policy or may breach compliance. 

Aim’s prompt protections also provide guardrails for safeguarding homegrown AI apps from attacks and malicious prompts with Aim’s AI Firewall. Security teams can verify that AI chat apps handle inappropriate use properly by testing their policies on real-time prompts in Aim’s playground to ensure protection.

Examples for supported requirements:

1. OWASP
   LLM02:2025 Sensitive Information Disclosure
   AI providers or developers must prevent unintended exposure of sensitive data in LLM applications through strict access controls, filtering, and redaction mechanisms.‍
2. MITRE ATLAS
   MITRE ATLAS:Exfiltration LLM Data LeakageAI providers or developers must detect and mitigate unauthorized extraction of sensitive data from LLMs to prevent information theft and adversarial exploitation.

1. NIST
   MEASURE 2.2: Evaluations involving human subjects meet applicable requirements (including human subject protection) and are representative of the relevant population
   AI providers or developers must ensure human subject evaluations comply with ethical guidelines, legal protections, and demographic representativeness for fairness and reliability.

Third party AI app sanctioning

AI applications that don't align with organizational AI policy or are suspected of regulatory breaches can have protective measures assigned to mitigate the risk of employee use. An app can be blocked if the organization deems it forbidden. Security teams can also enable limited use and set up tailored alerts to notify users of potentially risky AI interactions. Aim provides tailored 'Aim Recommendations' for each application, based on our risk assessment analysis, suggesting optimal configurations to help end users comply with organizational AI policies. Protections can be implemented immediately across the organization, allowing new risks to be addressed quickly.

Examples for supported requirements:

1. NIST
   GOVERN 6.1: Policies and procedures are in place that address AI risks associated with third-party entities, including risks of infringement of a third-party’s intellectual property or other rights.
   Policies must be implemented to manage third-party AI risks, addressing intellectual property infringement and ensuring compliance with legal and contractual obligations.
2. MITRE ATLAS
   ML Model AccesS: ML-Enabled Product or Service
   Access to machine learning models in deployed products must be secured to prevent unauthorized use, exploitation, or adversarial manipulation.

Detections

Continuous oversight of internal and external AI systems is a regulatory necessity, requiring human review of AI-generated content. Managing these numerous systems can be overwhelming. Aim simplifies this process by providing alerts for breaches of your organizational AI policy, such as irregular use of risky AI systems or detection of risky prompts by Aim's algorithms. These alerts ensure supervised access, monitored usage, and robust compliance.

Examples for supported requirements:

1. NIST
   MEASURE 2.6: The AI system is evaluated regularly for safety risks – as identified in the MAP function. The AI system to be deployed is demonstrated to be safe, its residual negative risk does not exceed the risk tolerance, and it can fail safely, particularly if made to operate beyond its knowledge limits. Safety metrics reflect system reliability and robustness, real-time monitoring, and response times for AI system failures.
   Regular safety evaluations must be conducted to ensure compliance with risk tolerance and to implement fail-safe mechanisms for reliability and robustness.
2. OWASP
   LLM02:2025 Sensitive Information Disclosure
   Sensitive data exposure must be prevented by enforcing strict controls, monitoring outputs, and mitigating unintended information leaks.

Aim’s Compliance Center

Having trouble navigating your organization's framework? Aim’s Compliance Center is here to help. Integrated into Aim’s platform, it lets you easily track which Aim offerings support regulatory compliance, such as the OWASP Top 10 for LLMs or MITRE ATLAS, while guiding you through the framework sections and how Aim addresses them.

Addressing key AI challenges, like managing complex systems, tackling algorithmic biases, and ensuring the security of AI applications across industries, is becoming increasingly crucial. Proactive risk management, collaboration, and a commitment to ethical AI development are vital for harnessing AI's full potential while mitigating risks. We anticipate further regulations to follow, and with Aim’s suite of tools for discovery, protection, and detection, along with its continuously updated Compliance Center, businesses can effectively navigate the complex landscape of AI regulation.

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