AI has become a mainstay in all organizations of all sizes. From simple use cases like ChatGPT and Bard to enhance employee productivity, to the creation of dedicated in-house AI tools to help with supply chain management and customer service, AI is quickly beginning to permeate all aspects of an organization’s operations.
As AI becomes more prevalent, the need for robust AI governance has never been greater. For all its benefits, the security risks of these tools cannot be ignored. Organizations need a framework for secure and ethical implementation.
Enter AI TRiSM—an architecture to help organizations responsibly adopt AI.
What is AI TRiSM?
AI TRiSM stands for AI trust, risk, and security management. The framework was created by Gartner to aid organizations in mitigating the multi-pronged risks surrounding AI deployment by focusing on three key focal points:
- Trust: Embedding confidence into AI’s output and decisions.
- Risk: Helping organizations identify AI-specific concerns (such as hallucinations, biases and data privacy) and then put in place strategies to manage them
- Security management: Securing data and AI systems from leakage, theft and manipulation.
Why is AI TRiSM important?
The AI TRiSM framework gives organizations a structured tried-and-tested approach for deploying safe, ethical and trustworthy AI. Here’s a closer look at the benefits:
- Overcome algorithmic bias: AI model biases can severely damage an organizations’ reputation and have severe repercussions for customers. Just take the Dutch taxation authority’s use of AI between 2016-2021. Their AI model incorrectly pinpointed thousands of families for committing fraud, when they in fact had not. These conclusions were reached based on unfair risk profiling embedded into the AI’s training data. With AI TRiSM in place, the Dutch taxation authority would’ve pinpointed these biases prior to the system’s release. Put simply, it would never have happened in the first place.
- Data breaches: AI systems rely on troves of sensitive data—the exposure of which would be catastrophic for organizations from a compliance and legal standpoint. AI TRiSM helps organizations mitigate the risks of AI data leakage and unauthorized usage.
- Regulatory compliance: AI TRiSM enables organizations to ensure their AI solutions meet regulatory requirements, preventing compliance fines and legal repercussions.
- Operational efficiency: Organisations that implement AI TRiSM stand to harness the full potential of AI. The analyst firm notes that, by 2026, organizations using the framework will achieve a 50% improvement in “adoption, business goals and user acceptance.”
The 4 Pillars of AI TRiSM
AI TRiSM encompasses four interrelated pillars that work together to boost trust, security and mitigate risk in AI systems.
Explainability
AI outputs can sometimes seem almost magical, with the AI arriving at a defined conclusion with little to no explanation on its reasoning. This, inherently, raises concerns around trust and accountability. If organizations don’t understand how AI came to an answer, it is harder to rely upon it. Explainability directly tackles this issue. It aims to shine a light on the AI’s decision-making process, so organizations can build trust. This is achieved by:
- Implementing robust AI governance frameworks to ensure reliable AI outputs.
- Creating real-time audit trails for AI outputs.
ModelOps
Model operations or ModelOps involves the use of automated and manual mechanisms to manage the AI’s performance. Gartner advises using version control to monitor AI models as they progress, and discover issues during the development phase. Testing is recommended during every stage of the AI model lifecycle, along with ongoing retraining to ensure the model remains relevant.
AI AppSec
AI applications are vulnerable to a range of novel threats that other applications are not. For example, AI agents can be hijacked through steganographic prompting, where threat actors hide malicious commands in the documents, emails, or web content that the agent processes.
AI AppSec prevents these risks through a multi-pronged security approach that takes into account tooling, software libraries and hardware.
Privacy
AI systems are dynamically exposed to sensitive data—from sensitive information that lives in training data to PII that is inputted into prompts or shared in documents. Ensuring this data is secure in line with compliance requirements is crucial for maintaining customer trust and avoiding hefty compliance fines.
AI TRiSM helps organizations put in place guardrails for secure AI adoption through strategies like:
- Mandated encryption of data at rest and in transit
- AI-specific data security posture monitored to prevent bi-directional data leakage.
- Dynamic access controls so AI tools have just-in-time access to sensitive information.
AI TRiSM with Polymer SecureRAG
Polymer’s SecureRAG complements AI TRiSM implementation. Our solution autonomously identifies historical and real-time data threats within LLM models, helping organizations embed trust, reduce risk and implement world-class security for their AI systems and sensitive information.
Discover how Polymer SecureRAG can empower you to embed AI TRiSM into your AI deployments.