AI agents promise enormous productivity gains, but without proper governance, they can quickly become a liability. As organisations deploy Agentforce and other AI tools, establishing robust governance frameworks is no longer optional—it’s essential.
At Purus Consultants, we help clients implement AI responsibly, balancing innovation with control. Here’s what you need to know about governing AI agents in your Salesforce environment.
Why AI Governance Matters
AI agents operate autonomously, making decisions and taking actions without constant human oversight. This autonomy is what makes them powerful—but it also introduces risks that traditional software doesn’t present.
Data Privacy – Agents access customer data to provide personalised responses. Without proper controls, they could inadvertently expose sensitive information.
Compliance – Regulations like GDPR, CCPA, and industry-specific requirements apply to AI-generated interactions. Non-compliance carries significant penalties.
Brand Risk – An agent making inappropriate or incorrect statements can damage your reputation instantly.
Security – AI systems are potential attack vectors. Malicious actors might attempt to manipulate agents through prompt injection or exploit access to sensitive systems.
Accountability – When something goes wrong, you need clear audit trails showing what the agent did and why.
Effective governance mitigates these risks whilst enabling innovation.
The AI Governance Framework
A comprehensive governance framework addresses five key areas:
1. Data Governance
- AI agents are only as good as their data. Poor data governance leads to inaccurate responses, biased recommendations, and compliance violations.
- Data Quality – Implement standards for accuracy, completeness, and consistency across all data sources that agents access.
- Data Classification – Clearly label data by sensitivity level (public, internal, confidential, restricted) and enforce access controls accordingly.
- Data Lineage – Maintain records of where data originates, how it’s transformed, and who can access it.
- Data Retention – Define how long different types of data should be kept and when it should be deleted.
- Consent Management – Ensure you have appropriate consent for how customer data is used by AI systems.
For Salesforce implementations, Data Cloud provides many of these capabilities natively, but you must configure them properly.
2. Access Controls and Permissions
Agents should operate under the principle of least privilege, accessing only the data and systems necessary for their function.
- Profile and Permission Sets – Define exactly what each agent user can see and do within Salesforce.
- Sharing Rules – Ensure agents respect your organisation’s record-level security model.
- Field-Level Security – Prevent agents from accessing sensitive fields they don’t need.
- External System Access – Carefully control what external systems agents can interact with and what credentials they use.
- Role-Based Access – Different agent types should have different permissions based on their function (customer service agents need different access than sales agents).
- Regular audits ensure permissions remain appropriate as your org evolves.
3. Monitoring and Auditing
You cannot govern what you cannot see. Comprehensive monitoring is essential. Here’s what needs to take place:
- Conversation Logging – Every interaction between users and agents should be logged for review.
- Action Tracking – When agents take actions (updating records, sending emails, triggering processes), these should be auditable.
- Performance Metrics – Track key indicators: response accuracy, escalation rates, user satisfaction, and error rates.
- Anomaly Detection – Identify unusual patterns that might indicate security issues or agent misconfiguration.
- Regular Reviews – Establish a cadence for reviewing agent performance and adjusting configurations as needed.
Salesforce provides built-in analytics for Agentforce, but you’ll likely want to supplement this with custom reporting based on your specific governance requirements.
4. Ethical Considerations
AI governance extends beyond technical controls to ethical principles, for example ,you want to ensure:
- Transparency – Users should know when they’re interacting with an AI agent rather than a human.
- Fairness – Agents should treat all users equally, without bias based on protected characteristics.
- Accountability – Clear ownership for agent behaviour and outcomes must be established.
- Human Oversight – Critical decisions should always involve human judgment, not be fully automated.
- Right to Explanation – Users should be able to understand why an agent made particular recommendations or took specific actions.
Our recommendation is to document your organisation’s ethical principles as soon as possible for AI and ensure agent configurations align with them.
5. Change Management and Documentation
AI systems evolve continuously. It’s really important that governance must accommodate change whilst maintaining control. We recommended baking in the the following principles:
- Version Control – Track changes to agent configurations, instructions, and actions.
- Testing Requirements – Define what testing is required before deploying agent changes.
- Approval Workflows – Establish who must approve modifications to agents before they go live.
- Documentation Standards – Maintain clear documentation of what each agent does, how it works, and what data it accesses.
- Rollback Procedures – Have plans for quickly reverting changes if issues arise.
Treating agent configurations with the same rigour as code deployments prevents many problems and is critical to long-term rollout success.
Compliance Considerations
Different industries face different regulatory requirements. Common considerations include:
- GDPR (EU) – Right to be forgotten, consent management, data minimisation, and transparent AI decision-making.
- CCPA (California) – Consumer rights to know what data is collected and how it’s used.
- HIPAA (Healthcare) – Strict controls on protected health information access and disclosure.
- PCI DSS (Payments) – Security requirements for systems handling payment card data.
- SOC 2 – Security, availability, and confidentiality controls for service organisations.
Your governance framework must address whichever regulations apply to your business. Work with legal and compliance teams to ensure agent implementations meet requirements.
Implementing Governance: Practical Steps
Step 1: Establish an AI Governance Council
Create a cross-functional team responsible for AI governance:
– Executive sponsor (provides authority and resources)
– Legal/compliance representative (ensures regulatory alignment)
– Security representative (addresses security concerns)
– Data governance lead (manages data quality and access)
– Business stakeholders (represent user needs)
– Technical lead (implements governance controls)
This council defines policies, reviews implementations, and addresses issues as they arise.
Step 2: Define Policies and Standards
Document clear policies covering:
– What types of agents can be deployed and by whom
– Data access requirements and restrictions
– Testing and approval processes
– Monitoring and audit requirements
– Incident response procedures
These policies should be specific enough to be actionable but flexible enough to accommodate different use cases.
Step 3: Implement Technical Controls
Configure Salesforce to enforce your policies:
– Set up appropriate profiles and permission sets
– Configure sharing rules and field-level security
– Implement audit logging and monitoring
– Create approval workflows for agent changes
– Establish testing sandboxes
Leverage Salesforce’s Einstein Trust Layer and Data Cloud governance features where possible.
Step 4: Train Your Team
Everyone involved in building or maintaining agents needs to understand governance requirements:
– What policies apply
– How to implement controls properly
– What to do when issues arise
– How to document their work appropriately
Governance only works if people understand and follow it.
Step 5: Monitor and Refine
Governance is not set-and-forget. Continuously:
– Review agent performance against governance KPIs
– Identify gaps or issues in current controls
– Adjust policies based on lessons learned
– Stay current with regulatory changes
– Adapt to new Salesforce features and capabilities
The Purus Approach
We help organisations implement AI governance that’s appropriate for their risk profile and regulatory environment. Our approach:
1. Assessment – Evaluate current governance maturity and identify gaps
2. Framework Design – Create policies and standards tailored to your business
3. Implementation – Configure technical controls in Salesforce
4. Enablement – Train teams on governance requirements
5. Ongoing Support – Provide continued guidance as your AI implementation evolves
Our goal is governance that enables innovation whilst managing risk—not bureaucracy that stifles your company’s progress.
AI governance requirements will continue to evolve as regulations develop and technology advances. Organisations that establish strong foundations now will be better positioned to adapt.
Salesforce is investing heavily in governance tools within Agentforce and the Einstein platform. Staying current with these capabilities ensures you’re leveraging the best available controls.
Ready to tackle Your Agentforce Implementation?
If you’re deploying Agentforce or planning to, now’s the time to establish your governance framework. Reactive governance after problems emerge is far more painful than proactive planning.
Get in touch to discuss your AI governance requirements and how we can help you deploy agents responsibly.