AI Governance

adaptAI will help you address some or all of these topics:

AI Governance strategy

Align strategy with intended business outcomes and technology. Encode policies into business rules and transparent reporting mechanisms to establish intentional clarity.

Organizational frameworks Establish AI ethics boards, COEs, iterative training, and communications strategies to operationalize best practice principles. Establish a structure for accountability. Ensure diversity in model selection, training, deployment, and monitoring.

AI Governance Training

We provide governance training for Boards and Sr. Management to understand and oversee the use of AI in the enterprise.

Our training covers risks and opportunities of AI and best practices of governance committees, COEs and policies.

Regulatory & risk advisory

Establish an AI risk management framework to ensure compliance with existing and upcoming laws.  Provide guidance on emerging cybersecurity threats and safeguards against them.

We perform audits and risk assessments to comply with EU AI Act, NIST AI RMF, Digital Services Act, ISO 42001, NYC HR Bias Law, and others. 

Data risk assessment

Enable data collection and transparent reporting to make needed information available to all stakeholders

Security services

We evaluate and provide solutions to implement Cyber Security controls for AI to prevent Adversarial Prompting, Data Poisoning etc.

User-prompts filtering

Training data for generative AI models and embeddings are continuously scanned for sensitive data attributes, then easily tagged. We will have prebuilt classifications and rules but your organization may have more. We’ll help you inject the right classifications and assign it the proper sensitivity attribute. Your organization can expand on these based on your unique requirements.

Synthetic Data

We will help build data sets free of bias and outliers to help tune the models.

Policy and Procedures

We will help write new SOPs or Policies that line up with your current corporate or regulatory standards

Vector Database creation, maintenance and protection

A vector database is a specialized type of database designed to store and retrieve data in the form of high-dimensional vector representations. These vectors are numerical encodings of the underlying data, capturing its essential features or attributes in a dense mathematical representation.

Each vector in the database is an n-dimensional vector, where n is the number of dimensions or features used to represent the data. The dimensionality can vary significantly, ranging from tens to thousands of dimensions, depending on the complexity and granularity of the data being encoded.

This data represents your corporate information in a form that a Large Language Model can understand and process. Each LLM will have its own unique vectorization technique. We will make sure this is placed in a secured place only available to your AI applications, Agents and Models.

Model-response filtering

In the context of training large language models (LLMs), we will use specialized techniques to improve the quality and safety of the model’s generated responses. This involves filtering out or modifying certain responses that are deemed inappropriate, harmful, or undesirable based on your predefined criteria or rules.

Model Auditing

Implementing an effective audit framework for Large Language Models (LLMs) is crucial to ensure responsible and ethical use of these powerful models. The audit framework should encompass both manual and automatic processes to comprehensively evaluate the model’s outputs, behaviors, and potential risks. We will introduce a simple yet important step in every AI development called HITL, or Human In The Loop.

Depending on what data, area of your business or functional responsibility of each user, we will develop a process to evaluate representative samples of the model’s output across the various domains.

We will also recommend proven independent AI products to help you audit your AI systems.