What is AI TRiSM?

AI TRiSM stands for Artificial Intelligence Trust, Risk, and Security Management. It is a framework designed to help organizations with the trustworthiness, security, and compliance of AI systems.

In recent years, organizations have started to implement AI technology in their operations. Without proper guardrails for these AI technologies, such as Microsoft Copilot, in place, these organizations risk financial, data, and reputational losses.

This is where AI TRiSM comes into play. By implementing AI TRiSM, businesses can:

• Build trust in AI-driven decisions.
• Reduce legal and reputational risks which could result from oversharing data.
• Improve AI security and resilience.
• Ensure AI aligns with various regulatory and compliance standards.

Key Components of the AI TRiSM framework:

1. Trust

AI models must be designed to be understandable by humans. Additionally, an organization’s data must be prepared in order to provide AI models with up to date and relevant data in order for it to provide meaningful results. AI TRiSM promotes transparency by requiring:

• Explainability – Ensuring AI models provide reasoning behind their decisions.
• Bias detection and mitigation – Identifying and reducing biases that may lead to unfair or discriminatory outcomes.
• Fairness audits – Regular evaluations to ensure AI systems do not disproportionately impact specific groups.

2. Risk management

AI introduces new risks such as security vulnerabilities, oversharing of data, and regulatory non-compliance. To minimize exposure to these vulnerabilities, AI TRiSM includes risk management strategies such as:

• AI Model validation – Continuous testing and validation of AI models to ensure consistent performance.
• Regulatory compliance – Adhering to legal and ethical guidelines, such as GDPR, HIPAA, or the EU AI Act.
• Incident response planning – Establishing security and multiple procedures to address potenital AI failures or breaches.

3. Security management

AI systems often handle sensitive data, making them a target for cyber threats, including data exfiltration. AI TRiSM integrates security measures to protect against threats, including:

• Data protection – Encryption and anonymization techniques to secure personal and business data.
• Access controls – Restricting access to AI models and datasets based on user roles.
• Adversarial attack defenses – Detecting and mitigating AI-specific threats, such as data poisoning or model inversion attacks.