Defensible AI in a Risk-Heavy World

The emergence of artificial intelligence has redefined the boundaries of what software can achieve in knowledge-based professions. From accelerating contract analysis to summarizing discovery documents, the promise of AI in legal, privacy, and compliance-driven domains is no longer theoretical—it is real. With the promise AI offers comes a critical dilemma: most mainstream AI models are not designed for environments where evidence, risk, and regulation intersect. Large language models (LLMs), while powerful in general-purpose applications, present structural challenges when deployed in domains that require explainability, accountability, and security. These models were not built with legal defensibility in mind, nor were they trained in environments where audit logs, human oversight, or data sovereignty are mandatory.

Agentic AI emerges as a compelling alternative.

Download this whitepaper to learn:

• What shortcomings of generative AI introduce risk that information security, legal, and privacy professionals cannot tolerate
• How domain-specific agentic AI systems can reduce the risks these professionals face while reducing costs, timelines, and risk
• How agentic AI might help facilitate rapid response to a cybersecurity incident like a data breach