AI agents have become instrumental in orchestrating digital ecosystems in businesses and optimizing workflows. Its advanced capabilities have exponentially expanded its market growth and are projected to rise further from $5.4 bn in 2024 to $50.31 bn by 2030. Their autonomous task execution and ability to merge complex processes reduce human errors and data unreliability by 95%. It is changing the definition of smart operation, customer service, and scalable experiences.
Businesses across banking and finance, healthcare, and the public sector can leverage AI agents to fuel powerful automation. However, without appropriate security and governance frameworks in place, this technological boon can quickly turn into a bane. In fact, 93% of leading security experts anticipate a rise in AI-driven attacks, including phishing and bot attacks due to malicious prompt injections, among others. Deploying secure AI agents is thus crucial for businesses to avoid risks such as uncontrolled data anomalies, data exposure, and theft, thereby enabling better compliance and auditability. Salesforce effectively addresses these challenges with a native security and governance framework.
Read the blog to explore Salesforce AI agent security best practices and learn how you can reap their benefits to your advantage.
Security and Governance Challenges of AI Agents
AI agents excel in self-learning and autonomous task completion with minimal intervention. Their independence, however, also creates multiple challenges, including:
- Autonomous agents can have unauthorized access to classified information, risking data leakage and exposure
- Unethical hacking and malicious prompt injection can lead AI agents to perform malevolent actions, sidestepping guardrails
- Poor security regulations, leading to non-compliance, costly penalties, and compromised reputation
- Unchecked shadow AI agent deployments risk data exfiltration and breaches
Salesforce AI Agent Security Best Practices
Employing Salesforce AI agent security best practices helps businesses to effectively address the above-mentioned challenges. However, a robust security-first approach, paired with the right suite of tools, aligned to your business goals, is imperative. Check out the following best practices helpful to build a compliant and secure ecosystem for AI agents:
- Determining Access Control and Permission of AI Agent
Create specific purpose-driven AI agents incorporating the Zero Trust principles and Principle of Least Privilege (PoLP). Restrict data accessibility based on each agent’s role and workflows. Increase the security level further with integrated step-up verifications such as biometric, OTP, and multi-factor authentication (MFA). Set the topics and the range of questions the agents can ask a user to perform a business function. For instance, a financial service AI agent, responsible for solving regular credit card payment issues, should be permissible to read only the ‘customer case’ and ‘payment’ disputes—not sensitive details such as the PINs and credit scores. Furthermore, routine review of the agent roles is mandatory to limit their unintended access expansion that occurs due to their self-learning attempts. - Securing Workflows and Actions
Companies dealing with classified and high-stakes data such as PII, loan records, and financial transaction history demand robust data security processes. Salesforce’s natively built governance tools, such as the Einstein Trust Layer, help financial and healthcare service providers in fulfilling this purpose. The Trust Layer serves as a trustworthy mediator that securely passes the customer’s NLP prompt inputs to the LLMs to generate and deliver the desired responses. The zero data retention feature, for instance, prevents the LLM from storing customer prompts and related information, avoiding the risk of unauthorized data spills. - Enforcing Continuous Monitoring
Another core Salesforce AI agent security best practice is constant monitoring as this facilitates a reliable and secure workflow across all business verticals. Continuous monitoring requires you to conduct routine reviews of AI agents’ access logs and audits to evaluate abnormal activities and prompt inputs. Allow ‘full event monitoring and logging’; this helps in reviewing the tools used, data access attempts, and so on. Additionally, it helps determine the need to modify the existing guardrails and compliance practices. A recent McKinsey & Co. study further highlights that CEOs and board members play a crucial role in determining the successful employment of AI agents’ adoption and workflow modernization. This implies the importance of a structured monitoring process that includes C-suite-level supervision. - Integrating Governance Control
Further solidify your data governance Salesforce Agentic AI security with continuous monitoring and runtime guardrail activities—necessary to integrate governance control. This helps in detecting potential threats and suspicious activity, ensuring a secured AI agent ecosystem that improves with time. - Employ an End-to-End Testing
Last, but not least, businesses need to adopt the culture of a comprehensive testing process of AI agent deployments. To strengthen the AI agent risk management Salesforce process, a hybrid approach involving automated testing and human review is paramount. While automation can accelerate quality evaluation, a final human review is important prior to AI agent deployment.
Organizations equipped with the proper Salesforce AI agent security best practices can reap significant benefits from AI agent implementation. However, establishing a reliable AI agent ecosystem requires an effective security and governance framework backed by expert insights. Attempting it without assistance can hinder your business’s productivity and momentum. Expert Salesforce professionals at Evoke Technologies can help you with this. Our customer-focused Salesforce solutions ensure you get past the security hurdles seamlessly to focus on implementing AI agents and drive value.