What is AI TRiSM?
AI TRiSM stands for Artificial Intelligence (AI) Trust, Risk and Security Management. Gartner® developed AI TRiSM as a framework enabling AI governance, reliability, fairness, efficacy and privacy. The framework concerns itself with ensuring appropriate safeguards and governance is employed to avoid inappropriate use of artificial intelligence.
Furthermore, Gartner® defines five pillars of AI TRiSM on which to build effective AI solutions:
- Explainability
- ModelOps
- Data Anomaly Detection
- Adversarial Attack Resistance
- Data Protection
Information technology leaders must spend time and resources supporting AI TRiSM. Supporting AI TRiSM will improve AI results regarding adoption, business goals, internal and external user acceptance. Moreover, threats and compromises – either malicious or benign – evolve. Keeping threat and compromise evolution in mind, AI TRiSM has to be a continuous effort.
Fabio Caversan, Stefanini’s Vice President of Innovations and Digital Business, believes that despite the hype around AI reaching human levels and the range of possible applications, many new products are being deployed. Hence, there are many unknown territories.
Furthermore, he says that it is a fact that AI systems are adaptive which means evaluating the system today will not be valid in the future.
AI TRiSM as a Trending Technology
Gartner® states:
“By 2026, organizations that operationalize AI transparency, trust and security will see their AI models achieve a 50% result improvement in terms of adoption, business goals and user acceptance. Gartner also predicts that by 2028, AI-driven machines will account for 20% of the global workforce and 40% of all economic productivity.”
The stage we are at with AI allows us to use and apply it in end-to-end applications and non-critical automation. For example, AI that recommends a book is not critical to human safety. Now, when it comes to essential decisions, AI can play a significant role in augmenting human skills – but the final decision must belong to humans.
AI TRiSM – Business Value
AI TRiSM has business value. Since AI needs new forms of controls that conventional tools cannot provide, new AI TRiSM creates dependability, trustworthiness, protection and privacy. It makes the best of trust by driving improved results regarding AI adoption and achieves business objectives.
In 2023, business and IT leaders should align priorities along the following three measures:
- Optimize operational sustainability by using AI technology to watch and predict system and user behavior, then apply them to prevent failures and autonomously adapt product and process capabilities
- Grow productivity and customer value: Mix industry-specific and optimized developer solutions and sensing technology to deliver and scale area-specific organizational value
- Innovate: pioneer productizing individualism by leveraging tech that supports unique customer expectations within communities such as social, economic or technologic
How AI TRiSM Works
The core goal of AI TRiSM is to optimize AI adoption for improved reliability and functionalities. Consider AI systems in production based on the following components:
Explainability – AI TRiSM should have information clearly explaining an AI model adoption. This helps organizations comprehend their AI model interpretation and their AI model’s performance in terms of accuracy, accountability and transparency.
ModelOps – Model Operationalization (ModelOps) is a function in AI TRiSM focused on end-to-end lifecycle management and analytics governance. AI and decision models include analytical models, models based on machine learning, knowledge graphs, linguistics, rules and more.
Data Anomaly Detection – In AI, data anomaly detection involves drift monitoring and detection of anomalies, errors and difficulties. This pillar of AI TRiSM assists organizations in performance improvement with full AI data visibility.
Hostile Attack Resistance – Attacks are AI threats involving deceptive data to change machine learning algorithms to disrupt AI functionality. AI TRiSM aims to detect and prevent adversarial assaults through detection, defense, artifacts localization and adversarial learning.
Data Protection – AI technology creates and uses an abundance of data. Protecting data is essential because data breaches can cause significant damage. This damage includes financial losses, reputation damage, security and health threats. AI TRiSM helps governance and regulations like the General Data Protection Regulation (GDPR).
Furthermore, it supplements data privacy with synthetic data, differential privacy and secure computing techniques like Secure Multi-Party Computation (SMPC) and Full Homomorphic Encryption (FHE).
AI TRiSM Challenges
Bias is a significant challenge in AI. Oftentimes, bias is caused by skewed, incomplete or non-representative data sets. AI systems assist in essential decisions related to hiring and medical diagnoses based on parameters that can include race and gender. Biases cause societal damage, and through biases, AI technologies have the potential to aggravate existing disparities that operate against our moral and legal systems.
National governments have increasing interests in AI regulation. They want to support AI’s potential while preventing it from getting out of control. Organizations must adhere to AI regulations and keep their AI systems secure and trustworthy.
To protect the public from algorithmic discrimination, there are different approaches to AI that avoid using massive datasets and considerable model training. In cases with discriminatory biases, it results from systems that need a tremendous amount of data for training.
During training, systems learn societal biases. There is a strong need for new approaches and new models to understand the world without generalizing patterns learned from large amounts of data.
Moreover, there are several key customer concerns related to AI:
- Bias against AI. Some people are biased against AI at this point in its development.
- Insufficient oversight. People are concerned about a lack of human oversight of AI systems.
- Unexpected behavior. This may possibly be influenced by pop culture such as seeing AI systems going rogue in movies.
- Misunderstanding. Many people are unfamiliar with how AI works and they are afraid of the unknown.
AI TRiSM is Important
AI regulations are developing, and organizations have significant incentives to support AI trust management. AI is a relatively new technology. Consumers’ AI knowledge, understanding and trust is relatively low. However, organizations can improve consumers AI trust levels.
Organizations can assist customers with their concerns by embracing AI TRiSM guidelines. Approaching AI trust this way can make systems less risky and increase transparency. The primary goal of AI TRiSM is to keep people secure and allow for growth and innovation.
Implementing AI TRiSM
Companies and organizations wanting to implement AI TRiSM should consider a comprehensive and multifaceted framework. In general, three steps related to documentation a system of checks and a high degree of transparency should drive the framework.
- Create substantial documentation rules and best practices. A strong documentation system supports trustworthiness by focusing on AI training data. Furthermore, these techniques help tech auditing if something goes wrong. Documentation should be founded on legal guidelines and internal risk evaluations, including standardized documentation practices and document templates. Also, the documentation system must be consistent and intuitive so it supports AI TRiSM and using AI technology.
- Have checks and balances. It is important for organizations to monitor potential bias and prevent a damaged system from causing damage. For example, automated features in a documentation system can alert if records are incomplete, missing or anomalous.
- Prioritize AI transparency. A lack of trust in AI technology often comes from misunderstandings. Some consumers view AI decision-making as it happens inside an indecipherable black box. Companies should address AI issues and transparency by helping non-technical consumers see how data is collected and how the system makes decisions.
AI TRiSM and Stefanini
Stefanini’s Natural Language Processing (NLP) framework is called Sophie. Created in 2009, Sophie was designed to be explainable and it still is today. This helps us immensely comply with AI TRiSM standards. Stefanini’s top priority is building trustworthy partnerships with clients. Trust and our agility helps us co-create solutions our customers can use to operate efficiently.
