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Introduction to AI TRiSM

The dependency of the world on new technologies is increasing day by day. AI is the prime candidate for consideration innovation of new applications and approaches to take the advancement to the next level.

Implementing AI in appliances and services has resulted in a more accessible and convenient life for many people. The applications range from smart home devices to mobile phones, toys, and machinery. AI promises a lot of advantages or benefits to keep the business competitive and drive business value faster with effective processes, as well as to reduce the operation cost significantly. Similarly, a concept arises, AI TRiSM, which marks the systems' reliability, trustworthiness, and security. Let’s discuss it in detail.

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What is AI TRiSM?

AI TRiSM, or Artificial Intelligence Trust, Risk, and Security Management, is a discipline or a framework that supports and enables AI Model governance, reliability, fairness, efficacy, privacy, data protection, and trustworthiness.

Gartner predicted AI TRiSM to be a trending technology in the upcoming years. By 2026, the organization that will incorporate AI transparency, trust, and security will see a 50% efficiency increase in their AI Model in terms of adoption, business goals, and user acceptance.

Also, by 2028, Gartner predicts that AI will handle about 20% workload, and 40% of the economy will be produced by AI and Automation approaches. AI TRiSM has Three Frameworks, named as:

    AI Trust
    AI Risk
    AI Security Management

What is the frameworks of AI TRiSM?

It works to enable Trust, Risk, and Security Management and hold capabilities to anticipate better business outcomes for AI Projects. The main frameworks that are followed for better reliability, security, and trustworthiness are:
AI Trust

This framework is associated with transparency or explainability, i.e., the ability to identify if the model achieved the desired outcomes with steps. This helps build trust and transparency.
AI Risk

Applying precise and strict governance in managing the Enterprise AI risks. Recording and Managing the development and process stages of the models and checking all parts of the release process to check the integrity and compliance.
AI Security Management

Ensuring Security at each stage of the process in the ML Model operations. AI Security Management is capable of getting access to the entire ML pipeline, identifying anomalies, automating the CI/CD Pipeline, and scanning vulnerabilities.

It is protecting the AI models and their functionality and helping generate better business outcomes with technological advancements and better adoption strategies.

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What are the major pillars of AI TRiSM?

There are 5 Basic Pillars of it, which hold the foundation of the AI Trust, Risk, and Security Management concept:-
Explainability

Explainability is the concept of marking every possible step to identify and monitor the states and processes of the ML Models. Simply put, the capability to detect or identify if the model has reached the target.

With this, organizations can monitor the performance of their AI models and propose improvements to make the process more efficient and generate better results with improved productivity.
ModelOps

ModelOps focuses on maintaining and managing the end-to-end lifecycle of every AI Model, including models based on analytics, knowledge graphs, decisioning, etc.
Data Anomaly Detection

As the name implies, this pillar focuses on detecting and identifying issues and helps AI practitioners see the full image of the Data issues to make effective decisions.
Adversarial Attack Resistance

Adversarial Attacks are AI attacks or threats that use data to disrupt Machine learning algorithms and alter the machine learning models' functionality. It detects and remediates these threats to ensure a streamlined process throughout.
Data Protection

The primary fuel source for machine learning models is data, so the better the data is secured, the better the operations and functionality there can be.

AI TRiSM ensures that there is preferred privacy and security of the data to stay in compliance with the regulations for data protection, such as GDPR