AI TRiSM (Trust, Risk, and Security Management): Focuses on frameworks to proactively manage risks associated with AI, ensuring safe and effective deployment in business contexts.

AI TRiSM (Trust, Risk, and Security Management): Focuses on frameworks to proactively manage risks associated with AI, ensuring safe and effective deployment in business contexts. This must be a real technical paper. Please make sure that It must be in the following format: Abstract, Keywords, Introduction, Proposed Methodology Block Diagram, Algorithm, Flow Chart, Result Analysis, Conclusion, and References. ———— May be the following infos will be useful: AI TRiSM (Trust, Risk, and Security Management) is an emerging framework that emphasizes the importance of managing risks, fostering trust, and ensuring security throughout the lifecycle of AI systems. It involves a combination of strategies, tools, and practices that help organizations deploy AI safely, ethically, and effectively while maintaining compliance and mitigating potential harms. Key Components of AI TRiSM: Trust Building and maintaining trust among stakeholders (customers, employees, regulators) through transparency, fairness, and ethical AI practices. Ensuring explainability and interpretability of AI models, so decisions made by AI systems are understandable and justifiable. Risk Management Identifying, assessing, and mitigating risks associated with AI models, including biases, inaccuracies, and unintended consequences. Monitoring AI systems to prevent harm or misuse, such as privacy violations, discrimination, or security vulnerabilities. Security Protecting AI systems and data from cyber threats, adversarial attacks, and unauthorized access. Ensuring robustness and resilience of AI models in dynamic and evolving threat landscapes. Key Practices in AI TRiSM: Bias Detection and Mitigation: Regularly testing AI systems for biases and implementing strategies to reduce them. Model Governance: Establishing policies and guidelines for AI model development, deployment, and monitoring. Ethical AI: Embedding ethical considerations in AI design, from data sourcing to decision-making processes. Continuous Monitoring: Ongoing evaluation of AI performance and its impacts on business outcomes and society. Compliance Assurance: Aligning AI systems with relevant legal, regulatory, and industry standards. Benefits of AI TRiSM: Increases stakeholder confidence in AI systems. Reduces the risk of reputational damage or regulatory penalties. Enhances the performance and reliability of AI models. Facilitates ethical and responsible AI adoption at scale.