Leveraging AI TRiSM for trustworthy and safe AI implementations

Embrace TRiSM for responsible AI: Why GRACE is your key

In today’s rapidly evolving digital landscape, the adoption of AI technologies is no longer just an option but a strategic imperative for enterprises seeking to remain competitive. However, as AI systems become more complex, the need for robust Trust, Risk, and Security Management (TRiSM) increases significantly. AI TRiSM, a term coined by Gartner, Inc., is a comprehensive program that helps proactively integrate governance from the outset. It ensures AI systems comply with regulations, remain fair, and protect data privacy.

As highlighted by Gartner’s Top Strategic Technology Trends for 2024, enterprises that integrate TRiSM controls into their AI projects not only achieve enhanced model precision and consistency but also improve business value and decision-making accuracy by mitigating bias and ensuring data integrity. This proactive approach to managing AI risks is no longer a luxury but a necessity to ensure sustainable and responsible AI utilization.

Gartner defines AI TRiSM as the practice of ensuring, “AI model governance, trustworthiness, fairness, reliability, robustness, efficacy, and data protection [through] solutions and techniques for model interpretability and explainability, AI data protection, model operations, and adversarial attack resistance.”¹

According to Gartner, “by 2026, organizations that operationalize AI transparency, trust and security will see their AI models achieve a 50% improvement in terms of adoption, business goals and user acceptance”.²

TRiSM helps manage AI risks, but which risk factors require the most urgent attention from organizations using AI?

Gartner’s 6 Drivers of Risk:

• Explainability and Model Monitoring: Stakeholders can struggle to clearly explain AI models to managers, users, and consumers.
• Generative AI Tools: Tools like ChatGPT can bring new, unanticipated risks that traditional controls can’t handle.
• Third-Party AI Tools and Data Confidentiality: Use of external AI tools can lead to data leaks and other security risks.
• Continuous Monitoring Requirement: AI models require ongoing scrutiny to ensure they remain compliant and ethical.
• Adversarial Attacks: AI models are susceptible to harmful attacks that require specialized security measures to prevent.
• Upcoming Regulations: New AI-specific regulations like the EU AI Act are in the pipeline, requiring organizations to prepare accordingly.³

The problems identified by Gartner echo many organizations’ concerns. Fortunately, they represent exactly the kind of challenge that 2021.AI’s GRACE Platform is designed to solve.

GRACE is a solution designed to empower enterprises to govern their AI projects effectively. GRACE facilitates comprehensive AI model governance, enabling businesses to manage model risks and ensure regulatory compliance effectively.

The platform aligns with Gartner’s prediction that by 2026, enterprises applying an AI TRiSM framework will see a significant increase in the accuracy of their AI-driven decisions. By embracing the GRACE AI Platform, organizations secure a robust safety net for their AI initiatives. GRACE’s governance frameworks and guardrails are engineered to ensure that the data is free from biases and aligned with ethical standards. This proactive approach is crucial for fair and transparent AI systems.

As the model progresses from development to deployment, GRACE’s continuous monitoring systems provide real-time insights into model behavior and decision-making processes. This allows for ongoing bias detection and correction, ensuring that as the model learns and evolves, it does not deviate from its ethical guidelines.

Furthermore, GRACE offers tools for explainability, which explain AI decisions for stakeholders, reinforcing trust and facilitating regulatory compliance. By making the decision-making processes of AI models understandable and relatable, ensuring that AI-driven applications and systems remain as transparent as possible, despite increasing complexity.

When AI models are implemented, GRACE helps organizations to sustain model accuracy and integrity, ensuring that outcomes remain consistent and reliable. This full-spectrum approach to AI model lifecycle management ensures that from inception to production, every phase is governed with the utmost attention to fairness, transparency, and security, safeguarding the AI investment and value for the organization.

If building and maintaining trust in AI are high on your agenda, it might be time to explore just what GRACE has to offer.

Interested in learning more? Contact us today.

Resources

1. https://www.gartner.com/en/information-technology/glossary/ai-trism
2. https://www.gartner.com/en/newsroom/press-releases/2023-09-27-gartner-says-cisos-need-to-champion-ai-trism-to-improve-ai-results
3. https://www.gartner.com/en/articles/what-it-takes-to-make-ai-safe-and-effective