Building Trust in Legal AI: Why Compliance and Security Can’t be Optional
Avinash Bonu
Head of Legal Tech
Iota Analytics
Jonathan Nystrom
Senior Advisor
Iota Analytics
As we reach the end of this series of blogposts, we may have left the best (or at the least the most important) thing for last. In previous posts we have covered right-sized LLMs, explainability and auditability, cost optimization, reliability and how to approach proof of concepts. If you’ve been reading, you have seen one topic being the most fundamental: trust.
As AI becomes increasingly embedded in legal workflows, the stakes around trust, transparency, and data protection are higher than ever. Legal teams are not only expected to deliver timely, accurate outcomes, but to do so while navigating a maze of regulatory requirements and guarding sensitive client information. In this environment, compliance and security aren’t just features. They’re the foundation.
The Hidden Risks of Black-Box AI
Many off-the-shelf AI solutions function as “black boxes,” producing results with little explanation or auditability. For legal professionals, that opacity isn’t just frustrating, it can be dangerous. Legal teams need to understand how AI reaches its conclusions, especially in high-stakes matters like litigation risk, contract obligations, or regulatory compliance.
“Transparency is non-negotiable in legal tech,” says Jonathan Nystrom. “If a legal team can’t explain an AI-driven result in court or to a regulator, that’s a major liability.”
Built for Compliance, Secured by Design
Beyond interpretability, security and regulatory compliance are core to how legal AI must operate. Sensitive legal data (ranging from privileged communications to regulatory filings) demands more than basic encryption. It requires end-to-end protection and deep alignment with data privacy laws.
“Our experience building hundreds of models has led to the belief that models must not only be built to perform,” says Avinash. “They must be built to comply. We ensure that jurisdiction-specific regulatory standards are met and are incorporated into the very architecture of our AI systems.”
This proactive approach means that responsible AI solutions are compliant with frameworks like GDPR, HIPAA, and other regional data governance standards from day one. Legal teams can trust that data is protected at every point in the workflow, from ingestion and processing to output and storage.
“We solve this by using the CurAIte framework as a starting point for each new legal AI project” says Avinash.
A Smarter Path Forward
Embedding compliance and security into the core of AI development doesn’t just reduce risk, it also builds long-term trust in the technology. As more legal departments shift to AI-enabled tools, they will favor solutions that are not only powerful but also provably safe and compliant.
“Trust is everything,” Jonathan adds. “Legal teams need to know that the AI working behind the scenes is not exposing them to new risks, they need to know it’s protecting them from them.”
In an era where AI capabilities are rapidly advancing, the real differentiator is responsible innovation. Compliance and security are no longer check-the-box requirements, they are strategic imperatives.
We hope you have enjoyed this series of blogposts. If you’d like to revisit any of the prior posts, you can find them in our Knowledge Centre. If you’d like to hear a conversation between Jonathan and Avinash on the topics covered over the past few weeks, stay tuned! We’ll be posting that soon on our website and LinkedIn. And if you want to stay up to date on everything related to legal AI and Iota Analytics, please follow us on LinkedIn.
