Get to Know Data Cloud Governance

Learning Objectives

After completing this unit, you’ll be able to:

Define Data Cloud Governance.
Explain the importance of data governance in a digital labor platform.
Learn how Data Cloud and Agentforce work together to ensure trusted governance.

Why is Data Governance Important?

With Data Cloud, your business can take advantage of trapped data from external systems, deliver more personalized customer experiences across your Customer 360, and power enterprise AI with Agentforce. But how do you ensure your Data Cloud remains secure, compliant, and well-governed?

As we move toward an AI-driven future, the workplace is evolving: Employees will manage other employees and oversee AI agents. This evolution comes with a surge in data volume and complexity. For AI to function effectively, it requires vast amounts of diverse data, including unstructured data (like documents and images) and conversational data (like chat logs and voice recordings). Businesses need unified, well-governed data to ensure AI systems can operate reliably and responsibly.

With this shift, trust in the digital labor workforce is more critical than ever. Businesses now operate in an era where governance, data privacy, and security, along with responsible AI use are paramount. Customers, employees, and stakeholders expect companies to safeguard their data, act transparently, and make ethical, accountable AI decisions.

With the rapidly evolving data landscape, there’s a gap between data, governance, and AI. Some examples include:

Data silos: Businesses face significant challenges in delivering seamless customer experiences when data is scattered across multiple systems—especially when much of it is stored in unstructured formats.
Inconsistent data governance: Today’s AI-driven landscape requires managing a diverse mix of structured and unstructured data, which makes data governance more complex.
Data integration complexities: Without a deeply integrated platform, accessing and acting on data becomes a constant struggle. Teams are left relying on disconnected platforms, creating bottlenecks and unnecessary dependencies.

Let’s see how Data Cloud Governance ensures your Data Cloud is well-governed.

What is Data Cloud Governance?

Data Cloud Governance is the Salesforce solution to Data Cloud governance and AI. It allows enterprises to break down data governance silos and establish a trusted, scalable, and well-governed Customer 360. Data Cloud Governance empowers the AI-driven future by accelerating customer success and building trust in Agentforce.

Agentforce lays the groundwork for automation, analytics, and predictive and generative autonomous agents, ensuring they function with accurate, secure, and trusted data. All of this is driven by the Salesforce Platform—open, extensible, interoperable, and deeply integrated—fueling the next iteration of AI with well-governed data at its core.

By implementing Data Cloud Governance, you can:

Eliminate data governance silos by ingesting structured and unstructured data from multiple sources, such as Zero Copy integrations, creating a dynamic, well-governed customer 360.
Promote customer success and build trust in Agentforce with high-quality, reliable, and well-governed data.
Govern Data Cloud data to ensure better customer experiences through automation, analytics, and both predictive and generative autonomous agents.

Data Cloud Governance consists of three main concepts: AI tagging and classification, policy-based governance, and data security.

AI Tagging and Classification

The challenge of enforcing governance consistently across different types of data—each with its own context, policies, and processes—requires a scalable solution. With tagging, unstructured and structured data is systematically labeled, allowing policies to be applied uniformly across all data usage patterns in Data Cloud.

Tagging can be done manually or automated with AI, making governance more efficient. AI-based tagging and classification takes this a step further by automating the labeling and organization of structured and unstructured data in agreement with business policies. Tag propagation ensures uniform tagging and classification across the data lifecycle in Data Cloud, driving consistency in how governance is applied across all surface areas.

Policy-Based Governance

The Salesforce Platform, being metadata-driven, enables customers to define policies and manage governance at scale with greater flexibility and efficiency. At the core of this policy-based governance framework is attribute-based access control (ABAC) through row, field, and object-level security (RLS, FLS, OLS). Rather than applying policies to individual users or specific objects, governance is managed through metadata attributes—tags—allowing policies to be enforced dynamically.

For example, instead of assigning permissions manually, you can define a dynamic masking policy that automatically masks personally identifiable information (PII) data for any user who lacks the required security clearance. Similarly, access policies are defined to limit and control access to sensitive data. This approach ensures governance remains consistent and scalable, even as data and user access change over time. It also means that ‌employees and agents can only access and take action on the data they’re permitted to see across the platform.

Data Security

With trust as Salesforce’s top priority, data security, privacy, and compliance are essential to maintaining customer confidence and meeting regulatory requirements. Data Cloud ensures that sensitive information remains protected at every stage. With Platform Encryption for Data Cloud, customers now have full control over the keys we use to encrypt the data.

Additionally, Private Connect for Data Cloud enables secure connectivity to virtual private clouds, preventing exposure to the public internet. As enterprises embrace the future of self-service agentic AI, they can trust that Agentforce operates on secure, well-governed data, ensuring AI-driven decisions are both reliable and compliant.

Use Case: Bloomington Caregivers

Bloomington Caregivers is a fictitious home healthcare agency dedicated to providing comprehensive, high-quality care for the elderly. With a deep passion for exceptional patient service, Bloomington Caregivers uses Data Cloud to unify patient data, streamline care coordination, and enhance patient engagement.

Office manager and Salesforce admin, Harryette Randall, has successfully implemented Agentforce to help its workforce. Now, AI agent Tina autonomously engages with patients, providers, and payers—resolving inquiries, providing summaries, and acting across multiple channels.

For example, if a patient requests their visit history, Tina, operating within proper permissions, can securely retrieve and deliver that information. However, if a patient asks for a treatment plan based on their symptoms, Data Cloud Governance policies ensure Tina, to the limitations of AI agents, escalates the requests to ‌healthcare professionals.

Behind the scenes, Data Cloud uses AI to tag and classify structured and unstructured data—patient records, chat logs, PDF uploads, and call transcripts—ensuring critical information is properly categorized. Harryette then defines access policies for both Tina, the AI agent, and service representatives. This results in Tina knowing when to transition to a human expert—maintaining compliance, security, and the highest standards of patient care.

By embedding policy-driven governance at scale, governance and AI are connected, and agents operate with security, transparency, and compliance, while still delivering efficient, intelligent automation. As the #1 trusted platform, Salesforce is paving the way for faster, ‌more reliable, and more personalized customer experiences with a native, well-governed platform built for the future.

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