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The First 3 Steps to Building Data and AI Governance

Sunil Senan
Infosys

In today’s data and AI driven world, enterprises across industries are utilizing AI to invent new business models, reimagine business and achieve efficiency in operations. However, enterprises may face challenges like flawed or biased AI decisions, sensitive data breaches and rising regulatory risks, if not controlled and governed. To mitigate these risks, enterprises must embrace robust principles of responsible AI and embed them across all layers of the AI ecosystem — data, model and usage.

  • Data: Ensure data used for training AI is accurate, representative, and free from biases, with strong privacy and security controls.
  • AI Models: Controls for accuracy, relevance, explainability and fairness along with security measures for trusted reliable AI models.
  • Usage and Consumption: Monitor AI initiatives continuously and apply moderation layers to ensure ethical and compliant outputs, maintaining trust in AI solutions.

Enterprises must establish controls across the three layers based on:

  • Trust: Trust policy and guardrails to make AI explainable, traceable, accurate, reproducible and accountable
  • Ethics: Ethics policy and procedures to ensure AI initiatives are fair, free of biases and are protecting fundamental human rights
  • Privacy: Ensure privacy remains at the center of all initiatives, preserving privacy of individuals
  • Compliance: Ensure lawful and auditable AI initiatives
  • Security: Robust and secure data and AI ecosystems

On these fundamentals, enterprises must take the first three concrete steps to ensure robust governance over AI and data initiatives:

Step 1: Outline an organization-wide AI Governance Strategy

Enterprise's leadership should clearly outline the organization's strategy, vision and mission to ensure that responsible data and AI consumption is the primary focus of all individuals idealizing, developing and consuming data and AI.

Enterprise should define comprehensive policies and procedures at enterprise level around the five principles, actively govern the initiatives and educate its employees.

Step 2: Establish Operating Model

Subsequently, the next area of focus should be the people, processes and technology involved in these initiatives.

  • Enterprises must set up a Governance CoE, identify all the personas responsible along with clear roles and responsibilities assigned to different personas.
  • Enterprises must set up robust framework to govern the data and AI initiatives along with monitoring to ensure governance and compliance with the evolving regulations.
  • Enterprises must modernize their tools and technologies to manage and govern the initiatives. (e.g. tools for assessment, controls implementation, audit and monitoring)

Step 3: Operationalize the data and AI Governance Framework

Enterprises at this stage will be ready to govern each and every data and AI initiative by design.

  • Enterprises should start with documenting the AI use cases with governance related fingerprints.
  • Next, enterprises must prioritize the use cases and assess the risk for each of the use cases to enforce appropriate control for governance.

These three steps help enterprises to ensure responsible and sustainable data and AI initiatives, leading to better brand value and customer satisfaction.

Sunil Senan is Global Head of Data, Analytics and AI at Infosys

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The First 3 Steps to Building Data and AI Governance

Sunil Senan
Infosys

In today’s data and AI driven world, enterprises across industries are utilizing AI to invent new business models, reimagine business and achieve efficiency in operations. However, enterprises may face challenges like flawed or biased AI decisions, sensitive data breaches and rising regulatory risks, if not controlled and governed. To mitigate these risks, enterprises must embrace robust principles of responsible AI and embed them across all layers of the AI ecosystem — data, model and usage.

  • Data: Ensure data used for training AI is accurate, representative, and free from biases, with strong privacy and security controls.
  • AI Models: Controls for accuracy, relevance, explainability and fairness along with security measures for trusted reliable AI models.
  • Usage and Consumption: Monitor AI initiatives continuously and apply moderation layers to ensure ethical and compliant outputs, maintaining trust in AI solutions.

Enterprises must establish controls across the three layers based on:

  • Trust: Trust policy and guardrails to make AI explainable, traceable, accurate, reproducible and accountable
  • Ethics: Ethics policy and procedures to ensure AI initiatives are fair, free of biases and are protecting fundamental human rights
  • Privacy: Ensure privacy remains at the center of all initiatives, preserving privacy of individuals
  • Compliance: Ensure lawful and auditable AI initiatives
  • Security: Robust and secure data and AI ecosystems

On these fundamentals, enterprises must take the first three concrete steps to ensure robust governance over AI and data initiatives:

Step 1: Outline an organization-wide AI Governance Strategy

Enterprise's leadership should clearly outline the organization's strategy, vision and mission to ensure that responsible data and AI consumption is the primary focus of all individuals idealizing, developing and consuming data and AI.

Enterprise should define comprehensive policies and procedures at enterprise level around the five principles, actively govern the initiatives and educate its employees.

Step 2: Establish Operating Model

Subsequently, the next area of focus should be the people, processes and technology involved in these initiatives.

  • Enterprises must set up a Governance CoE, identify all the personas responsible along with clear roles and responsibilities assigned to different personas.
  • Enterprises must set up robust framework to govern the data and AI initiatives along with monitoring to ensure governance and compliance with the evolving regulations.
  • Enterprises must modernize their tools and technologies to manage and govern the initiatives. (e.g. tools for assessment, controls implementation, audit and monitoring)

Step 3: Operationalize the data and AI Governance Framework

Enterprises at this stage will be ready to govern each and every data and AI initiative by design.

  • Enterprises should start with documenting the AI use cases with governance related fingerprints.
  • Next, enterprises must prioritize the use cases and assess the risk for each of the use cases to enforce appropriate control for governance.

These three steps help enterprises to ensure responsible and sustainable data and AI initiatives, leading to better brand value and customer satisfaction.

Sunil Senan is Global Head of Data, Analytics and AI at Infosys

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The Latest

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IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...