AI agent security: 2026 checklist

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Companies are at different stages in their AI journeys, as we recently learned from Airtable research that included 1,001 organizations. At 56%, the majority are still in early stages of AI use, where employees use AI for individual productivity but not across systems and complex agentic workflows.

Yet there are some who have moved past pilots and put AI agents into production, and there are many planning to. Gartner projects that 40% of enterprise applications will embed task-specific AI agents by 2026, up from less than 5% in 2025. There is pressure not to be left behind, but these implementations come with some very real security concerns. Agents are browsing the web, writing code, querying databases, sending emails, and making decisions across your business, often without a human in the loop. The problem isn't that agents are inherently unsafe, but that many organizations are implementing them without true visibility into the actions they take and without sufficient AI agent security policies in place.

What is AI agent security?

AI agent security is the set of controls, policies, and practices that govern how autonomous AI agents access data, take actions, and interact with other systems, including how humans maintain visibility and oversight. As a discipline, agent security identity and access management, data handling and permissions, observability into agent behavior, and governance frameworks that define what agents are and aren't allowed to do.

Traditional cybersecurity focuses on protecting users and infrastructure from external threats. Agents present a new challenge: What happens when trusted, internal AI systems can act autonomously at scale? An agent with access to your CRM, email, and collaboration tools is more than just another productivity tool. It has the power to make decisions that come with risk, can make costly mistakes, and is therefore subject to compliance and governance.

Understanding key AI agent security threats

These are the most significant threat categories organizations face as they scale AI agent deployments.

Prompt injection: This is the top vulnerability on the 2025 OWASP Top 10 for LLM Applications. Prompt injection occurs when malicious instructions are embedded in content an agent reads, such as an email, a document, a web page, causing it to take unintended actions. Because agents may process external content as part of their workflows, this attack can bypass traditional security controls.
Excessive permissions and privilege escalation: Only 21% of executives report complete visibility into what permissions their agents actually have. When agents are granted broad access, which is easier to configure than scoped access, a single compromised or misbehaving agent can traverse systems it was never meant to touch.
Shadow AI and unmonitored deployments: Organizations talk about the challenge of managing agent sprawl, but less about the risk that shadow AI poses. Shadow AI refers to use of AI tools that aren’t authorized, and more than 76% of organizations cite this as a definite or probable problem, up from 61% the prior year. Agents deployed outside IT governance have no audit trail, no permissions review, and no oversight when something goes wrong.
Data exfiltration through agent workflows: Agents compromised by hackers or malware can read, copy, and transmit sensitive data. Hackers may use agents to steal internal information, underscoring the importance of human-in-the-loop design and agent observability.
Supply chain and MCP vulnerabilities: Your attack surface expands as agents connect to external services and tools through protocols like MCP (Model Context Protocol). Compromised credentials or vulnerable third-party integrations can expose your entire agent ecosystem.
Insufficient observability: When you can't see what an agent did, why it did it, or what data it touched, you can't catch honest mistakes or deliberate attacks until it's too late. Most enterprise security stacks weren't built to monitor autonomous AI behavior in real time and the industry is catching up.

On a tool-by-tool basis, however, your team does have control over selecting solutions that meet compliance and enterprise-grade security requirements, and that allow you to observe agent behavior, set guardrails, and advance your multi-agent systems with confidence.

3 AI agent security measures to take now

While we’re all actively learning how to work best with AI, we can learn from some early AI pilot mistakes and take advantage of emerging best practices.

1. Scope and define agent permissions

Most agent security failures happen at the permissions layer. An agent that can read (view) everything, write (update) to everything, and connect to all key tools is a much higher security risk than one with tightly scoped access. It’s kind of like giving a very smart intern the keys to your kingdom. Generally, you wouldn’t do that.

Apply least-privilege principles the same way you would for human users: agents should only have access to the data they need for their specific task. Define those boundaries at setup. If you’ve already got agents running, then:

Audit what data and systems each agent can access today
Scope permissions to the minimum required for the agent's function
Separate read and write access wherever possible
Review agent permissions whenever workflows change or agents are repurposed

2. Build observability into every agent workflow

You can't govern what you can't see. Observability means maintaining a clear, continuous record of what agents are doing — what actions they take, what data they access, and what decisions they make — so that you can detect anomalies, investigate incidents, and demonstrate compliance.

This means designing workflows so that agent outputs flow through visible, reviewable stages across a shared operational surface that your team can monitor. It also means producing audit trails that capture both what happened and why. Your AI solutions need to:

Implement logging for all agent actions and data access
Create review checkpoints for high-stakes agent decisions (financial transactions, external communications, record updates)
Set up alerting for unusual agent behavior patterns
Ensure audit trails are tamper-resistant and accessible to security teams

3. Define and enforce governance before you scale

Governance can’t be an afterthought. You may be anxious to pull ahead or secure productivity wins, but you need to trust agents in production. That means developing clear policies about what agents are allowed to do, who can deploy them, and how they're reviewed and updated over time.

A governance framework establishes rules that hopefully come before agent sprawl. This can look like:

Documenting approved use cases for each agent and a plan to enforce scope
Requiring IT review and approval before agents access sensitive systems
Establishing a clear owner for AI security policy and agent lifecycle management
Creating incident response procedures specific to agent failures or compromises
Planning for regulatory requirements and updates

How Airtable provides observability and governance

The security challenges of AI agents aren't a reason to halt progress, but they are a reason to deploy more thoughtfully. It can take a little time to set up your AI agents for success, but if you take the time, you stand to realize both productivity gains and the assurance that you can quickly troubleshoot anything that goes awry. Solutions like Airtable provide your teams with a shared system of record where humans and agents work side by side. Your team has full visibility into what agents are doing and why, can control what agents access or edit, and step in when needed.

Learn more about building agent workflows you can trust

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Frequently asked questions

Treat AI agents like privileged users, and apply least-privilege access, which means scoping each agent’s permissions to exactly what it needs for its task and nothing more. Require authentication for every system the agent touches, and audit permissions regularly.

Three controls matter most: scoped permissions (agents can only access what they need), observability (you can see what agents are doing in real time), and governance policies (clear rules about what agents can and can't do, who can deploy them, and how they're reviewed).

Airtable is purpose-built for human-agent collaboration at enterprise scale, with permissions, audit trails, compliance controls (including SOC 2 Type II), and SSO built in, along with the observability needed to see what agents are doing across your operations. These are the capabilities you need to look for in any solution you adopt.

The most effective risk reduction comes from getting the fundamentals right before you scale: scope permissions tightly, build observability into every workflow, establish governance policies, and require human review for all high-stakes decisions. As you move forward, conduct regular audits of your agent inventory.

Agent security matters because agents have the power to make decisions and act autonomously at scale, potentially accessing vast stores of data. Failures may come from attacks, misconfiguration, or a model that makes an error, so it’s imperative to establish visibility and guardrails so that humans have a chance to intervene before consequences multiply.

About the author

Airtableis the AI-native platform that is the easiest way for teams to build trusted AI apps to accelerate business operations and deploy embedded AI agents at enterprise scale. Across every industry, leading enterprises trust Airtable to power workflows and transform their most critical business processes in product operations, marketing operations, and more – all with the power of AI built-in. More than 500,000 organizations, including 80% of the Fortune 100, rely on Airtable's AI-native platform to accelerate work, automate complex workflows, and turn the power of AI into measurable business impact.