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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

Hot Topics

The Latest

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...