The rapid adoption of AI in critical industries like healthcare and legal services has highlighted the urgent need for robust risk mitigation mechanisms. While domain-specific AI agents offer efficiency, they often lack transparency and accountability, raising concerns about safety, reliability, and compliance. The stakes are high, as AI failures in these sectors can lead to catastrophic outcomes, including loss of life, legal repercussions, and significant financial and reputational damage. Current solutions, such as regulatory frameworks and quality assurance protocols, provide only partial protection against the multifaceted risks associated with AI deployment. This situation underscores the necessity for an innovative approach that combines comprehensive risk assessment with financial safeguards to ensure the responsible and secure implementation of AI technologies across high-stakes industries.

Regain information autonomy by bringing people closer to the source of truth.

VaultX is an AI-powered middleware solution designed for real-time detection, encryption, and secure management of Personally Identifiable Information (PII). By integrating regex, NER, and Language Models, VaultX ensures accuracy and scalability, seamlessly integrating into workflows like chatbots, web forms, and document processing. It helps businesses comply with global data privacy laws while safeguarding sensitive data from breaches and misuse.

In a post-AGI future, misaligned AI systems risk harmful consequences, especially with control over critical infrastructure. The Alignment Compliance Framework (ACF) ensures ethical AI adherence using .align files, Alignment Testing, and Decentralized Identifiers (DIDs). This scalable, decentralized system integrates alignment into development and lifecycle monitoring. ACF offers secure libraries, no-code tools for AI creation, regulatory compliance, continuous monitoring, and advisory services, promoting safer, commercially viable AI deployment.

Even with Reinforcement Learning from Human or AI Feedback (RLHF/RLAIF)

to avoid harmful outputs, fine-tuned Large Language Models (LLMs) often present insufficient

refusals due to adversarial attacks causing them to revert to reveal harmful knowledge from

pre-training. Machine unlearning has emerged as an alternative, aiming to remove harmful

knowledge permanently, but it relies on explicitly anticipating threats, leaving models exposed

to unforeseen risks. This project introduces model scoping, a novel approach to apply a least

privilege mindset to LLM safety and limit interactions to a predefined domain. By narrowing

the model’s operational domain, model scoping reduces susceptibility to adversarial prompts

and unforeseen misuse. This strategy offers a more robust framework for safe AI deployment in

unpredictable, evolving environments.