What is AI TRiSM?: Strategies for scaling secure and trustworthy enterprise AI

The rapid spread of generative artificial intelligence (GenAI) is raising concerns about security risks and the trustworthiness of GenAI solutions. From the security perspective, organizations need to ensure their data and intellectual property (IP) remain safe when using AI and that solutions align with their ethics and values. From the perspective of the user and general public, trust and safety are important, as enterprises may harvest personal data from the web and user inputs to train LLMs. The novelty of enterprise AI, paried with its rapid adoption, demands a fresh approach to address these concerns. 

Gartner established the AI Trust, Risk, and Security Management (AI TRiSM) framework to proactively address these concerns. Enterprises can implement AI TRiSM to better secure AI systems, foster stakeholder trust, and adhere to evolving privacy regulations. Other benefits include improved operational efficiencies, higher levels of user acceptance, and greater adoption of AI.

What is AI TRiSM? 

AI TRiSM helps organizations evaluate the security and trustworthiness of their AI solutions and develop more risk-averse AI models and practices. In Gartner’s words, AI TRiSM, “ensures AI model governance, trustworthiness, fairness, reliability, robustness, efficacy, and data protection.” The framework offers enterprises a systematic approach to AI risk management, which remains uncharted territory for many organizations.

AI TRiSM is founded on four main pillars:

1. Explainability and model monitoring. Organizations must establish policies and procedures to improve AI transparency and assess model performance over time. The goal is to clearly explain how AI systems function, what models are likely to output, and where their strengths and weaknesses lie. Enterprises can use several strategies to support this pillar, such as model introspection, human feedback, or explainable AI (XAI) techniques. Businesses can also enhance AI interpretability through less complex model structures like decision trees when appropriate for a given task. 
2. Model operations (ModelOps). Gartner recommends organizations establish systems for managing the entire AI lifecycle, from data preparation to model development, deployment, monitoring, and feedback. Within the ModelOps pillar, enterprises require additional frameworks for governing processes like designing AI infrastructure, validating data quality, and benchmarking ethical standards. Organizations may have to develop proprietary ModelOps solutions since commercial tools are limited. 
3. AI application security. Organizations need robust application security tools to safeguard against AI-specific risks. Best practices include establishing strict access controls for AI systems, encrypting model files and data at every stage of the AI lifecycle, and employing red teams to test AI system security in inference. Companies can also use AI itself to keep models secure. Techniques like adversarial training enable models to recognize malicious prompts, while AI can help detect training data anomalies. 
4. Privacy. Application security, ModelOps, and explainability strategies work together to ensure enterprise AI complies with data protection laws and user privacy expectations. As part of the AI TRiSM framework, enterprises can address privacy concerns by routinely auditing models for compliance and communicating explainability and security measures to stakeholders. Employing legal and ethics experts helps enterprises stay aware of and enforce evolving privacy standards.

By supporting more secure, reliable, and transparent AI systems, AI TRiSM allows enterprises to comply with emerging regulations and foster customer and stakeholder trust. The framework can also improve overall business performance by boosting operational efficiency, supporting key innovations, and cementing an enterprise’s reputation as a leader in responsible AI. According to Gartner, businesses that operationalize AI transparency, trust, and security will see a 50% improvement in user acceptance, adoption, and business goals by 2026. 

Understanding the trust and security implications of GenAI 

An approach like AI TRiSM is crucial for deploying enterprise AI. The technology’s inherent risks are beyond what conventional security and IT controls can handle. Opaque algorithms can negatively affect user perceptions and an enterprise’s ability to comply with regulations, while the size of models and nature of their training can result in outputs that cause unexpected harm.  

Models are susceptible to new forms of attack 

AI models have unique security vulnerabilities, including prompt-based and ​​data-poisoning attack vectors. Adversaries can use these methods to access proprietary information and manipulate AI behaviors without directly infiltrating a model or its stored data. 

Models lack transparency 

Due to the complexity of AI systems, especially large neural networks, models have opaque algorithms and reasoning processes. This black box phenomenon makes it difficult to identify issues like bias, predict likely outputs, or understand how a model uses data. This uncertainty impacts user trust and raises regulatory compliance issues. For example, model opacity can affect compliance with the General Data Protection Regulation (GDPR), which requires businesses to report on what data their AI tools use and how a model uses it. 

Model outputs can be harmful  

Additionally, AI outputs reflect the scope and quality of their training data. GenAI tools have the potential to reproduce and magnify bias present in training datasets, expose sensitive training data, and ​​hallucinate in response to training knowledge gaps. As a result, AI systems may cause harm through incorrect, biased, or data-disclosing outputs, jeopardizing both trust and security. 

4 best practices for scaling the AI TRiSM framework  

AI attack vectors, capabilities, and legal requirements are in constant flux, making it difficult to maintain AI TRiSM while scaling AI deployments. However, hiring subject matter experts and focusing on workforce education, for example, makes it easier to adapt and scale.  

1. Enlist a diverse team of experts 

Navigating responsible and trustworthy AI deployments is a complex process requiring expertise in several disciplines, from AI software development to legal compliance. When scaling AI TRiSM controls, establish a well-rounded task force with experience in AI explainability, security, ModelOps, and privacy. The team should include professionals such as legal experts, ethicists, data scientists, and AI security experts. 

2. Focus on workplace education and culture 

AI TRiSM principles are more impactful when integrated throughout an enterprise’s culture rather than restricted to a niche task force. As part of your AI TRiSM strategy, educate staff on the implications of GenAI deployment and the value of operationalizing AI trust and security management. This further supports workforce buy-in and trust in your transformation initiatives. With the right capabilities and awareness, employees also gain the skills to mitigate enterprise risks collectively. 

3. Go beyond the minimum legal requirements 

Organizations must comply with precedent-setting regulations like the GDPR and the European Union AI Act. However, enterprises implementing AI TRiSM should view these laws as baseline standards and instead aim higher when developing strategies for secure and trustworthy AI. Start by clearly defining your enterprise’s values, ethics, and goals beyond the typical objectives of increasing revenue and shareholder value. For instance, create guidelines for what constitutes fair and ethical AI use in your organization or how your enterprise aims to enhance society in the long term through AI. 

4. Stay up-to-date and agile 

AI’s rapid acceleration demands enterprises to be agile and capable of adapting to new risks. Implementing AI TRiSM is an iterative process requiring a constant evaluation of internal AI systems and external technologies and standards. To this end, embrace a continuous learning mindset, reading widely, studying research papers, and joining conversations on new topics in AI security, ethics, and law.  

Navigate the evolving AI landscape more confidently with AI TRiSM 

AI TRiSM is a relatively new concept in the field of AI, giving enterprises a structured approach to deploying responsible and secure solutions. This framework provides necessary guidance for organizations new to AI adoption, especially those unfamiliar with AI-specific ethics and security issues.

Through AI TRiSM, enterprises can better understand how their solutions perform and continuously improve AI safety, trustworthiness, legal compliance, and alignment with enterprise values. While the framework serves as an effective foundation for developing responsible AI, organizations must evaluate their requirements and consult with experts to find specific solutions within each AI TRiSM pillar.

Learn more about what security risks to expect when developing and deploying LLMs.