AI Deployment
Solutions in this category support the responsible, secure deployment of artificial intelligence already in use within legal and business operations, rather than building AI models from scratch. Capabilities include verifying AI output against a trusted source of record before action, applying guardrails that block prompts or outputs involving privileged or restricted content, enforcing client specific rules such as outside counsel guidelines and ethical walls across every prompt and agent action, and monitoring usage across an organization by user, matter, tool, and use case. Other solutions focus on the data and infrastructure layer underneath deployed AI, providing governed, permission bound, and audited access to firm content for AI tools to draw on, or generating realistic, privacy safe synthetic data to test, train, and benchmark AI tools without exposing real client information. A broader subset addresses AI governance more generally, maintaining a registry of AI systems in use, classifying risk, and automating compliance documentation for frameworks such as the EU AI Act, ISO 42001, and GDPR, sometimes across an entire enterprise rather than legal work specifically. As legal teams increasingly rely on generative AI and agents that act with less direct human review, these tools provide the oversight layer needed to use AI safely, verify outputs, track performance, and maintain accountability, including through certificates, audit trails, and usage records that can be produced to clients, regulators, or courts.
Solutions in this category support the responsible, secure deployment of artificial intelligence already in use within legal and business operations, rather than building AI models from scratch. Capabilities include verifying AI output against a trusted source of record before action, applying guardrails that block prompts or outputs involving privileged or restricted content, enforcing client specific rules such as outside counsel guidelines and ethical walls across every prompt and agent action, and monitoring usage across an organization by user, matter, tool, and use case. Other solutions focus on the data and infrastructure layer underneath deployed AI, providing governed, permission bound, and audited access to firm content for AI tools to draw on, or generating realistic, privacy safe synthetic data to test, train, and benchmark AI tools without exposing real client information. A broader subset addresses AI governance more generally, maintaining a registry of AI systems in use, classifying risk, and automating compliance documentation for frameworks such as the EU AI Act, ISO 42001, and GDPR, sometimes across an entire enterprise rather than legal work specifically. As legal teams increasingly rely on generative AI and agents that act with less direct human review, these tools provide the oversight layer needed to use AI safely, verify outputs, track performance, and maintain accountability, including through certificates, audit trails, and usage records that can be produced to clients, regulators, or courts.
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