Use AI with
clear responsibility.

Name business and technical owners, with executive oversight. Accountability belongs to the organization deploying AI and cannot be assigned to the model itself.

Responsibility should remain identifiable when a project moves from development into everyday use. A business owner needs a way to act on failures, supported by technical ownership and executive oversight. Kashyap’s accountability article explains how clear roles and correction mechanisms connect governance to business value. Read more about building accountability into AI.

People need the expertise, information and authority to intervene. Define approval boundaries, escalation routes and fallback arrangements before granting agents autonomy.

A reviewer who cannot challenge an output or stop an action provides little protection. Set permissions and escalation rules before deployment, and make intervention part of the workflow. Kashyap’s work on managing agentic teams describes the management practices required when people supervise systems capable of taking actions. Read more about managing human and AI teams.

AI governance and privacy should be coordinated. What data a system accesses, why it uses that data and who can authorize access are central to both disciplines.

The connection becomes especially important when AI accesses information across multiple systems. Oversight must consider the purpose of access, permissions and the consequences of reuse, rather than treating privacy as a separate document. Kashyap’s article with Tom Walat explains the need to integrate these responsibilities. Read more about AI governance and privacy.

A tool can support monitoring and records, but responsibility requires people and processes. Integrate controls into existing AI workflows rather than treating a new platform as the solution.

Start by identifying the decisions and controls the organization needs to support. A platform is useful when it helps people carry out that work and preserve an appropriate record. Kashyap’s review of governance tools places their capabilities in the context of the organization’s broader oversight needs. Read more about selecting AI governance tools.

Inventory proposed uses, define acceptable data and actions, assess risk and assign owners. Establish monitoring and a way to respond when behavior or vendor terms change.

An initial assessment should be followed by operational checks. A system’s behavior, information sources and use can change, so review cannot end at approval. Kashyap’s risk-management article sets out the need for monitoring, defined remediation ownership and attention to changing third-party services. Read more about managing language-model risks.