Your Guide to AI Agent Security

To understand AI agent security, it helps to first define what an AI agent is. Unlike a basic chatbot that responds to predefined inputs, an AI agent can perceive its environment and interpret context as it works toward certain goals. Instead of merely reacting, an AI agent plans and adapts based on outcomes.

AI agents use a mix of real-time data, experiences, and predefined rules to make decisions. That might mean answering a complicated customer inquiry or adjusting pricing based on market trends. These agents can even coordinate with other systems to resolve a task — with minimal human input. Greater autonomy is beneficial in many scenarios, but it also adds complexity: the more freedom an agent has to make decisions, the more carefully you need to manage how it behaves.

AI agent security can help address these new challenges. It refers to the set of practices and tools that protect AI agents from both internal and external threats without limiting their usefulness. The goal is to create agents that are not only effective, but also trustworthy and compliant with data privacy regulations.

The safeguards used in AI agent security include:

• Input filtering
  This screens and verifies the data sent to an agent so that harmful, misleading, or unexpected inputs don’t influence its decisions.
• Access controls
  The right access controls define who and what can interact with an agent, preventing unauthorized systems from sending instructions or retrieving sensitive data.
• Audit trails
  Recording an agent’s actions and decision paths makes it easier to trace behavior, investigate incidents, and confirm compliance.
• Model and policy validation
  Validation is used to confirm that the underlying model and its guardrails behave as intended before and after deployment.
• Data protection
  Applies encryption and data-handling standards to keep private information safe as the agent processes, stores, or transfers it.

Of course, security doesn’t stop at technical controls. It also requires clear policies for agent behavior, such as defined boundaries for decision-making and built-in mechanisms for human oversight.

Salesforce’s security best practices offer a strong foundation, but securing AI agents requires going a step further by treating them as dynamic entities, not static applications. The more autonomous and intelligent the agent, the more intentional your security strategy needs to be.

There are several types of AI agents, each designed to support specific parts of your business. Since they vary in how they gather information, make decisions, and complete tasks, the security protections you need will vary as well. Understanding the agent types you use can help you focus on the right safeguards.

To keep this simple, it helps to look at agent types by the level of access they require and the kinds of decisions they make.

These agents work closely with personal information and often act on behalf of customer-facing roles, which means the security focus centers on data privacy, identity protection, and consistent responses.

• Customer service agents
  Help resolve questions and issues across channels. They need protections that keep customer data private, maintain accurate context across conversations, and prevent unauthorized instructions from shaping their response.
• Ecommerce agents
  Guide customers through purchases and recommend products. These agents benefit from safeguards that protect transaction details, keep recommendations fair, and prevent outsiders from influencing buying paths.

These agents often access sensitive business data, such as lead scoring, campaign plans, and performance insights. Security centers on protecting internal information and preventing unintended disclosures.

• Sales development representative (SDR) agents: Assist with lead qualification and meeting scheduling. They need permission controls that limit which customer records they can touch and monitoring that confirms they follow your internal sales policies.
• Marketing campaign agents: Help create campaign briefs, customer journeys, and content. These agents require safeguards that protect strategic plans, maintain brand alignment, and verify that generated assets follow approved guidelines.

These agents combine information from multiple systems, so the security focus shifts toward managing system boundaries and confirming that cross-department workflows stay accurate.

• Collaboration agents: Share data and insights across functions so that roles like operations, service, and planning stay aligned. These agents benefit from guardrails that define which systems they can access and controls that prevent them from moving information where it doesn’t belong.

Across all types, it is essential that you design with the agent’s purpose in mind. The more adaptive and autonomous the agent, the stronger your protections should be. From limited-scope reflex agents to fully autonomous learners, your security strategy should scale based on their complexity.

Where traditional application security focuses on code and infrastructure, AI agents introduce an entirely new dimension: autonomous behavior. That brings new challenges and new risks that must be addressed with targeted protections.

AI agents often process highly sensitive data to make decisions, including customer information and financial records. If this data isn’t properly secured, it can lead to unauthorized access, data leaks, or compliance violations. When AI agents are embedded in workflows that involve personal or regulated data, data privacy must become a top priority.

AI agents are powered by large language models (LLMs) that learn from data, but these models can be manipulated. Attacks like data poisoning, adversarial inputs, or unauthorized model changes can distort outcomes. In extreme cases, they can even cause agents to act against your organization's objectives.. Since models are dynamic by design, it's important to regularly validate both their inputs and their decision logic.

Unlike traditional software, agents do more than just follow fixed instructions. They evaluate conditions and weigh outcomes, before acting based on probabilities. This makes their behavior less predictable — especially in multi-step interactions. When you can’t see how an AI agent makes decisions, it’s much harder to spot when something goes wrong or figure out why it happened in the first place.

AI agents don’t operate in isolation. They depend on surrounding systems, APIs, and infrastructure. A vulnerability in one component (such as a third-party integration or cloud environment) can become an entry point for attacks that compromise the agent. Protecting the environment around the agent is just as important as securing the agent itself.

AI agents often interact with users, third-party tools, and external data sources in real time. But these connections can introduce security risks. If input isn’t properly validated, attackers can exploit the agent through techniques like injection attacks, feeding it misinformation, or misusing exposed APIs. Without safeguards in place, an agent might act on inaccurate data or unintentionally give outsiders access to internal systems.

Securing AI agents is an ongoing process that spans from development and deployment to daily operations. Every layer of the stack (including access control and ethical governance) needs built-in protections tailored to the dynamic nature of AI agents.

One foundational step is controlling access. Assign clear roles and permissions to both users and external systems that interact with the agent. Track non-human identities and make sure each action can be audited. This helps prevent unauthorized use and sets the stage for AI accountability.

Encryption also plays a vital role. Use advanced encryption techniques to protect data that AI agents generate or process. That includes both structured and unstructured data across storage, transit, and use — without degrading agent performance. Consider isolating agents using sandboxing or containerization. By limiting their reach, you reduce the risk of cascading damage from unintended actions or external exploits.

Remember that security isn’t just the responsibility of developers or IT. It’s a shared commitment across product teams and business leaders. Everyone involved in the app development process should understand how agents function, what data they handle, and how to flag unexpected behaviors. Building a culture of accountability strengthens every layer of your AI strategy.

AI agent hijacking happens when malicious actors manipulate or take control of an agent’s behavior. This can stem from poisoned training data or weak security controls. Recognizing attack methods means knowing what to look for. Unexpected decisions, changes in agent behavior, or unusual data access patterns may indicate something’s wrong. Stay alert to adversarial techniques that attempt to “trick” the agent into acting inappropriately.

Strengthening security measures often involves proactive evaluations. Perform regular risk assessments and monitor agent activity to catch potential hijacking attempts before they escalate. Building resilient frameworks helps agents bounce back from threats. Use adaptive security models that learn from incidents and automatically adjust protections to neutralize future attacks.

Stay up to date with global frameworks like GDPR and CCPA, both of which define how personal data must be handled and protected. Even if your AI agent isn’t customer-facing, it may still interact with regulated data behind the scenes. That’s why you should use ethical AI frameworks to maintain fairness, transparency, and accountability in every decision your agent makes. Design systems that explain their reasoning and provide audit trails when needed.

Most importantly, manage data with care. Apply privacy policies and data security standards consistently across every interaction. Whether you’re training a model or deploying it into production, privacy should be part of your default architecture.

The best security starts as early as the design stage. Design AI with security first by integrating protective measures directly into the development process. You can do this by using secure coding practices, as well as threat modeling and testing before your agent ever goes live.

Secure multi-agent environments by establishing controls that prevent one agent from exploiting another. That means enforcing communication protocols and limiting shared access. Monitor and respond to threats through real-time observability. Track usage patterns and activate a response plan when behavior goes out of bounds. Salesforce’s approach to AI privacy highlights how thoughtful design can improve both security and trust.

As AI agents become more embedded in enterprise systems, their security will only grow more complex — and more essential. New capabilities mean new vulnerabilities, so keeping up requires a forward-looking strategy.

Improving AI security isn’t something any one company can do alone. It takes collaboration across the industry. When organizations share security insights and best practices, everyone benefits. Working together helps set stronger standards that make AI adoption safer for everyone.

Agentforce, Salesforce’s enterprise agentic AI solution, helps you apply intelligent automation while keeping security and privacy at the center of every interaction.

With the Agentforce Trust Layer, your business can reduce risk through features like zero data retention and policy-driven controls. The Trust Layer also identifies and masks sensitive information in your prompts before sending them to the model, protecting sensitive data like PII from being exposed to external LLMs. These protections raise the baseline for AI agent security.