AI TRiSM: A framework for Trust, Risk and Security Management - Deepstash
AI TRiSM: A framework for Trust, Risk and Security Management

AI TRiSM: A framework for Trust, Risk and Security Management

Curated from: pytechacademy.medium.com

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In an era where artificial intelligence (AI) is rapidly being integrated into various aspects of our lives, ensuring trust, managing risks and maintaining security in AI systems is really important. Enter AI TRiSM (Artificial Intelligence Trust, Risk, and Security Management), a framework developed by Gartner to address the challenges associated with AI Governance.

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Organizations that incorporate AI TRiSM framework into business operations of AI models can see a 50% improvement in adoption rates due to the model’s accuracy.

Organizations leveraging AI TRiSM can expect significant benefits, including the removal of up to 80% of inaccurate or fake data by 2026.

GARTNER

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Understanding AI TRiSM

Understanding AI TRiSM

AI Trust: Building confidence in AI systems

It ensures ethical and fair practices are followed throughout the development and deployment process

AI Risk: Mitigating potential risks and pitfalls

Proactive risk management strategies are implemented to minimize negative impacts on stakeholders and society as a whole.

AI Security Management: Safeguarding AI systems and data

It focuses on protecting AI systems and data from cyber threats and vulnerabilities. Data privacy and compliance measures are enforced, and security is a priority for AI models, algorithms, and infrastructure.

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AI TRiSM is a framework that relies on 4 key areas to achieve its goals - Explainability, ModelOps, App Security and Data Privacy

KEY AREAS

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Explainability

Explainability

AI TRiSM enables organizations to understand how AI models make decisions. It is important for ensuring transparency and identifying potential biases which in turn promotes trust and accountability in AI systems.

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Model Ops

Model Ops

 Just like traditional software systems, AI models also require management throughout their lifecycle. AI TRiSM offers tools and processes for automating and monitoring the development, deployment, and maintenance of AI models, known as ModelOps. This ensures models remain effective and up-to-date in dynamic environments, mitigating risks associated with outdated models.

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AI App security

AI App security

As AI applications become more prevelant, they require robust security measures. AI TRiSM addresses this by implementing measures to protect against unauthorized access, data breaches, and malicious attacks, safeguarding both the applications and the sensitive data they handle.

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Data Privacy

Data Privacy

With the increasing use of AI comes the need to protect sensitive data and ensure compliance with privacy regulations. AI TRiSM assists organizations in implementing robust data privacy measures, including data encryption, access controls, and anonymization techniques, to protect individuals’ privacy rights and maintain regulatory compliance.

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Best practices for organisations

  • Form an AI taskforce
  • Prioritise explainability:
  • Implement continuous monitoring
  • Regular training and education
  • Stay informed about regulations
  • Collaborate with external experts

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The Danish Business Authority’s ethical AI protocol shows AI TRiSM in action. By monitoring models and testing for fairness, they managed billions in financial transactions. This example highlights how AI TRiSM helps entrepreneurs succeed in the AI market by ensuring accuracy, navigating regulations, and building trust.

USECASE

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Conclusion

AI TRiSM offers a structured approach to address these risks, providing organizations with the confidence to develop and deploy AI solutions responsibly. By prioritizing trust, risk management, and security, organizations can unlock the full benefits of AI while safeguarding against potential pitfalls.

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IDEAS CURATED BY

rajaathota72

Serial Entrepreneur | AI ML and Blockchain

CURATOR'S NOTE

Learn about AI TRiSM framework of Gartner

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