Tackling Trust, Risk and Security in AI Models

AI models and applications can pose significant risks if left unchecked. AI TRiSM provides proactive solutions to identify and mitigate these risks, ensuring reliability, trustworthiness and security.

Reduce AI risk with effective AI governance

Given its complexity and the fact that it’s a new discipline that organizations are often ill-prepared to handle, AI governance can seem overwhelming. However, organizations that adopt consistent AI risk management practices can avoid project failures and reduce potential security, financial and reputational damage. 

Regardless of the risk, AI governance and risk management remain an afterthought for organizations. That means teams often fail to consider the impact until models or applications are already in production. Governance can be difficult to retrofit into existing AI workflows, creating potential risks and inefficient workflows.

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    Understand AI risks and create a governance strategy

    Organizations must align people and technology when developing an AI governance strategy to manage risks effectively and at an organizational level from the get-go.

    Build an effective AI governance framework

    There are two primary risks of using AI. First, the compromise of sensitive data through oversharing, overexposure and a lack of controls to maintain privacy and data protection. Second, inaccurate, illegal, hallucinatory or other unwanted results that lead to bad outcomes for enterprise users if not stopped in their tracks.

    AI leaders should follow this governance framework:

    1. Establish AI accountability and define enterprise policies. 

    2. Discover and inventory all AI applications in the organization.

    3. Enhance AI data classification, protection and access management.

    4. Implement AI TRiSM technology to support and enforce policies.

    5. Conduct ongoing governance, monitoring, validation, testing and compliance.

    How AI TRiSM supports AI governance

    AI trust, risk and security management (AI TRiSM) ensures governance, trustworthiness, fairness, reliability and data protection in AI deployments. It supports enterprise AI governance policies through a shared responsibility model involving both users and providers.

    AI TRiSM includes five key technology functions: 

    • AI runtime inspection and enforcement and AI governance focused on real-time AI interactions, models and applications, with governance functions operating offline.

    • Information governance and infrastructure and stack, supporting both AI and non-AI environments. 

    • Traditional technology protection, which is to say non-AI-specific protection functions.

    Actions for AI leaders to mitigate AI risk

    Ensure robust governance across all AI technologies used within your organizations by:

    1. Defining enterprise AI policies that align with ethical standards, regulatory compliance and risk tolerance. 

    2. Auditing and enhancing AI information governance, focusing on data protection, classification and access management. This is an important prerequisite step to improve your baseline data protection and access controls and get your organization ready for the inclusion of AI tools.

    3. Implementing AI TRiSM technology to enforce policies and mitigate AI-related risks. 

    4. Using AI TRiSM for continuous governance, monitoring, validation testing and compliance

    AI risk FAQs

    What is AI TRiSM?

    AI trust, risk and security management (AI TRiSM) ensures AI governance, trustworthiness, fairness, reliability, robustness, efficacy and data protection. AI TRiSM includes solutions and techniques for model and application transparency, content anomaly detection, AI data protection, model and application monitoring and operations, adversarial attack resistance and AI application security.

    Drive stronger performance on your mission-critical priorities.