Skip to main content

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

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

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 ...

Despite the frustrations, every engineer we spoke with ultimately affirmed the value and power of OpenTelemetry. The "sucks" moments are often the flip side of its greatest strengths ... Part 2 of this blog covers the powerful advantages and breakthroughs — the "OTel Rocks" moments ...

OpenTelemetry (OTel) arrived with a grand promise: a unified, vendor-neutral standard for observability data (traces, metrics, logs) that would free engineers from vendor lock-in and provide deeper insights into complex systems ... No powerful technology comes without its challenges, and OpenTelemetry is no exception. The engineers we spoke with were frank about the friction points they've encountered ...

Enterprises are turning to AI-powered software platforms to make IT management more intelligent and ensure their systems and technology meet business needs for efficiency, lowers costs and innovation, according to new research from Information Services Group ...

The power of Kubernetes lies in its ability to orchestrate containerized applications with unparalleled efficiency. Yet, this power comes at a cost: the dynamic, distributed, and ephemeral nature of its architecture creates a monitoring challenge akin to tracking a constantly shifting, interconnected network of fleeting entities ... Due to the dynamic and complex nature of Kubernetes, monitoring poses a substantial challenge for DevOps and platform engineers. Here are the primary obstacles ...

The perception of IT has undergone a remarkable transformation in recent years. What was once viewed primarily as a cost center has transformed into a pivotal force driving business innovation and market leadership ... As someone who has witnessed and helped drive this evolution, it's become clear to me that the most successful organizations share a common thread: they've mastered the art of leveraging IT advancements to achieve measurable business outcomes ...

More than half (51%) of companies are already leveraging AI agents, according to the PagerDuty Agentic AI Survey. Agentic AI adoption is poised to accelerate faster than generative AI (GenAI) while reshaping automation and decision-making across industries ...

Image
Pagerduty

 

Real privacy protection thanks to technology and processes is often portrayed as too hard and too costly to implement. So the most common strategy is to do as little as possible just to conform to formal requirements of current and incoming regulations. This is a missed opportunity ...

The expanding use of AI is driving enterprise interest in data operations (DataOps) to orchestrate data integration and processing and improve data quality and validity, according to a new report from Information Services Group (ISG) ...

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

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

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 ...

Despite the frustrations, every engineer we spoke with ultimately affirmed the value and power of OpenTelemetry. The "sucks" moments are often the flip side of its greatest strengths ... Part 2 of this blog covers the powerful advantages and breakthroughs — the "OTel Rocks" moments ...

OpenTelemetry (OTel) arrived with a grand promise: a unified, vendor-neutral standard for observability data (traces, metrics, logs) that would free engineers from vendor lock-in and provide deeper insights into complex systems ... No powerful technology comes without its challenges, and OpenTelemetry is no exception. The engineers we spoke with were frank about the friction points they've encountered ...

Enterprises are turning to AI-powered software platforms to make IT management more intelligent and ensure their systems and technology meet business needs for efficiency, lowers costs and innovation, according to new research from Information Services Group ...

The power of Kubernetes lies in its ability to orchestrate containerized applications with unparalleled efficiency. Yet, this power comes at a cost: the dynamic, distributed, and ephemeral nature of its architecture creates a monitoring challenge akin to tracking a constantly shifting, interconnected network of fleeting entities ... Due to the dynamic and complex nature of Kubernetes, monitoring poses a substantial challenge for DevOps and platform engineers. Here are the primary obstacles ...

The perception of IT has undergone a remarkable transformation in recent years. What was once viewed primarily as a cost center has transformed into a pivotal force driving business innovation and market leadership ... As someone who has witnessed and helped drive this evolution, it's become clear to me that the most successful organizations share a common thread: they've mastered the art of leveraging IT advancements to achieve measurable business outcomes ...

More than half (51%) of companies are already leveraging AI agents, according to the PagerDuty Agentic AI Survey. Agentic AI adoption is poised to accelerate faster than generative AI (GenAI) while reshaping automation and decision-making across industries ...

Image
Pagerduty

 

Real privacy protection thanks to technology and processes is often portrayed as too hard and too costly to implement. So the most common strategy is to do as little as possible just to conform to formal requirements of current and incoming regulations. This is a missed opportunity ...

The expanding use of AI is driving enterprise interest in data operations (DataOps) to orchestrate data integration and processing and improve data quality and validity, according to a new report from Information Services Group (ISG) ...