Every time I sit next to a stranger at a coffee shop, I get a mild panic that they're there to secretly watch my keystrokes and steal my identity. And yet—I've embedded AI into my browser, my inbox, and basically every other corner of my work life.
The reason I'm not having that same panic about AI as I am about the stranger who was simply looking for a spot to sit and enjoy his coffee is because of the guardrails I've set for AI tools at work. I still panic that AI will come for my job, but not my identity.
If you're also letting AI live in every nook and cranny of your workday, here are the seven biggest AI security risks to keep in mind—and what you can do to manage them.
Table of contents:
7 AI security risks and how to manage them
When it comes to managing AI security risks, you could take the why-worry-about-one-thing-when-you-can-worry-about-everything approach (a personal favorite). Or—as thousands of dollars in therapy has taught me—you can let your Spidey senses rest a little and focus on the specific threats actually worth worrying about.
Here are the biggest AI security risks and what you can do to mitigate them.
1. Shadow AI
Seventy percent of employees report working without AI policies, guidance, or clarity, leaving them to experiment in the shadows—putting security, productivity, and trust at risk. The use of AI tools without official approval or governance is known as shadow AI.
The problem compounds quickly. Each shadow AI tool is a potential data leak point or compliance gap. Multiply that by hundreds of employees and dozens of different tools, and you have a significant attack surface that your security team doesn't even know exists.
How to manage this AI security risk
Create clear AI usage policies that people will actually follow. Tell employees which AI tools are approved and, more importantly, why these specific tools made the cut. Make it easy to request approval for new tools. If the approval process takes three weeks and requires five signatures, people will work around it. Speed and clarity prevent shadow AI better than bureaucracy.
Centralize AI access through governed infrastructure. When you provide AI agents with app access through infrastructure like Zapier, you create a single, governed path for AI to securely interact with your apps. Employees get the AI capabilities they want, and can access it from whatever entry point they want—whether that's Zapier MCP in a chat app, Zapier SDK in a coding agent, or Zapier CLI in their terminal. And IT teams maintain visibility and control over what's being accessed. No one needs to sneak around because the approved path actually works.
Provide approved alternatives that solve real problems. If employees are using shadow AI, they're trying to accomplish something. Give them sanctioned tools that meet those needs. Removing access to an unapproved tool without offering an alternative just drives the behavior underground, where you have even less visibility.
2. Data leaks
Getting good results from AI tools requires context—like product messaging guides, user personas, and company brand guidelines. The problem is that the line between helpful context and confidential information isn't always obvious.
One study analyzed over one million AI prompts and 20,000 file uploads across 300 generative AI tools, and found sensitive corporate data, including intellectual property, strategic plans, and customer data, in over 4% of prompts and over 20% of uploaded files. Once that information enters an AI system, you lose control over how it's stored, accessed, or potentially used to train future models.
How to manage this AI security risk
Implement technical safeguards before data reaches AI tools. Before information is shared with AI platforms, anonymize the data by stripping out personally identifiable information (PII), and deploy data loss prevention (DLP) controls that scan and redact sensitive content in real time. AI Guardrails by Zapier does this automatically: it scans for personally identifiable information, toxic language, prompt injection attempts, or negative sentiment—then routes, blocks, or escalates based on what it finds.
Choose AI platforms with strong data protection commitments. When evaluating AI tools, look for platforms with short retention periods that automatically purge data after a defined timeframe. Read their privacy terms carefully—you want explicit language stating data won't be used for training models. If it's vague or buried in legalese, that's intentional.
Automate workflows to eliminate the need for AI data processing. If workflows involve data management, automate them. When you use Zapier to integrate AI across thousands of apps, data flows directly where it needs to go through OAuth-managed connections with centralized access control. No one needs to copy and paste Q2's earnings report in ChatGPT because the workflow handles it automatically.
3. Credential theft
AI services have become prime targets for credential theft. Last year alone, over 300,000 ChatGPT credential sets were advertised on the dark web, which tells you everything you need to know about how valuable these accounts have become to attackers.
If a hacker steals someone's ChatGPT login, for example, they get instant access to months of conversation history, uploaded documents, and sometimes connected applications. And if an API key is leaked, it can lead to massive security issues and financial drain.
How to manage this AI security risk
Enforce strong authentication for AI tools. Use strong passwords with multi-factor authentication for every AI tool you adopt. Or, for a more secure alternative, use passkeys.
Monitor what AI apps connect to your network. Deploy a network auditing tool to see what AI services your employees are actually using. This way, you can spot unauthorized AI tools before they become a security incident. While you're at it, keep your software and operating systems up to date. Unpatched systems are how attackers get in to steal credentials in the first place.
Consolidate to fewer AI platforms. The more AI tool accounts exist across your organization, the more credentials attackers can target. Standardizing on a smaller set of approved tools reduces your attack surface significantly. For example, when you route everything through Zapier, you only enter your API key once, and Zapier's enterprise-grade security manages the rest, so you don't risk pasting API keys into your Claude chat for the hundredth time.
4. Code vulnerabilities
AI-generated code often contains security flaws that go well beyond laughable hallucinations. AI models can be tricked into misclassifying dangerous input as safe, potentially allowing malware to execute or bypass security controls entirely. If AI coding tools are used without proper supervision and human review, these vulnerabilities can slip directly into production code.
How to manage this AI security risk
Maintain endpoint protection and backup systems. Deploy anti-virus and anti-malware endpoint protection software to catch vulnerabilities that slip through. Pair this with a solid backup system so you have a recovery path when something breaks or gets compromised.
Reduce your custom integration attack surface. Every custom integration you build is another surface area to defend and monitor, and every MCP server from an untrusted source is a potential backdoor. The math is simple: fewer custom integrations means fewer vulnerabilities to track.
Use pre-built, maintained integrations. Pre-built integrations from established providers handle the ongoing maintenance centrally—auth flows, API patches, and OAuth debugging. Zapier's 9,000+ app integrations, for example, are maintained for you. So if Google changes their scope or Salesforce updates their API, your integrations keep working without intervention.
5. Prompt injection attacks
Prompt injection is when someone manipulates an AI system by crafting malicious inputs that override the system's instructions. Attackers use this to extract training data, manipulate AI agents into performing unauthorized tasks, and bypass content filters.
The risk gets serious when AI systems have access to databases or automation tools. A successful prompt injection could trigger actions across your infrastructure, like deleting database records or initiating unauthorized transactions.
How to manage this AI security risk
Implement input validation and sanitization. Filter and validate user inputs before they reach your AI systems. Set up allowlists for expected input patterns and automatically reject anything that falls outside those boundaries. Pay special attention to inputs that look like they're trying to override the AI's instructions—those are almost always malicious. Again, this is somewhere AI Guardrails by Zapier can automate the process.
Limit AI system permissions to what they actually need. Apply the principle of least privilege. Your customer service chatbot doesn't need access to your financial database, even if it would be convenient. Give AI agents only the data and actions required for their specific function. When prompt injection happens, limited permissions mean limited damage. On Zapier, you can pick exactly which apps and which actions your AI tools can access, so you're not giving your agents a master key to everything and the pool cabana.
Monitor AI system behavior and set up alerts. Log all AI interactions so you can review them later—or, even better, choose a tool like Zapier that automatically logs those interactions for you. More importantly, set up alerts for unusual patterns, such as attempts to access restricted data or commands that fall outside the expected scope. These anomalies often signal that someone's testing your defenses.
6. Insecure AI applications
AI tools can be built in about the same time it takes my dog to rip apart a brand new stuffed animal (alarmingly fast). With AI coding tools, anyone can wrap a simple interface around an existing AI model, market it as a specialized tool, and launch it in a matter of hours. This level of AI democratization broadens the scope of who can build software and solve real problems quickly. That's a good thing.
But it comes with tradeoffs. For example, an eager startup builds an AI-powered contract analysis tool in a weekend by wrapping a UI around the latest OpenAI model. The app works, so it ships—often without formal security audits, clear data handling protocols, or incident response plans. Organizations adopting these tools then inherit that risk.
How to manage this AI security risk
Vet new AI tools before adoption. Evaluate the company's reputation and track record. Then dig into their privacy policy and security features, paying special attention to how and if your data will be used to train their models or surface in other users' responses.
Use established infrastructure for app connectivity. Instead of adopting every new point solution that promises to connect your apps, use proven infrastructure. Zapier allows you to build safely with AI on infrastructure that's been hardened over more than a decade, across thousands of apps—all OAuth-managed.
Request security attestation. Ask the vendor for a letter showing their app security has been tested by verified third parties. If they can't provide one, you have your answer about how seriously they take security.
7. Deepfake fraud
Voice and facial recognition are increasingly used as access control security measures, but AI has made it easier than ever to bypass them. Deepfake technology used to require expensive equipment and specialized expertise. Now anyone can create a convincing deepfake with consumer-grade tools and publicly available models. This means biometric authentication alone is no longer sufficient protection for high-stakes transactions or access to sensitive systems.
How to manage this AI security risk
Layer authentication beyond biometrics. Biometric authentication is convenient, but it shouldn't be your only line of defense. For high-stakes actions like financial transfers or credential changes, require multi-factor authentication that combines biometrics with passwords or security keys. It's harder to fake multiple authentication factors.
Establish verification protocols for unusual requests. Create a culture where people verify unusual requests through a separate channel, even when they seem to come from authorized individuals. Your CFO asks you to wire funds to a new vendor? Verify through a text, phone call, or in-person conversation before acting.
Build auditable approval workflows. For high-value requests, set up human-in-the-loop workflows that log every step and require multiple sign-offs across different channels. If a deepfake attempt fools one person, additional approval requirements stop it. If fraud does occur, the audit trail shows exactly where the deception happened.
Build safely with AI and Zapier
AI security risks are real, but so is the solution: governed infrastructure. Every risk I've outlined here becomes more manageable when you build on infrastructure designed with security and governance at the core.
When you give AI agents access to your apps through Zapier, you're using OAuth-managed connections—which means the AI model never sees your actual credentials. This shifts the security model: instead of scattering API keys and passwords across different platforms, you connect each app once through Zapier and control all access from a single dashboard. If an employee leaves or a device gets compromised, you revoke access in one place. And because you're building on 9,000+ pre-built integrations, you're not constantly vetting new tools or maintaining custom code that could introduce vulnerabilities.
You can install Zapier into whatever environment you're building in. Working in chat apps like Claude and ChatGPT? Install Zapier MCP to give those AI assistants secure access to your apps. Building in code editors like Cursor? Install the Zapier SDK so your coding agents can connect to your tools. And use Zapier CLI if you're working in a terminal. Either way, you maintain visibility and control over what's being accessed.
Related reading
Zero trust security: What it is and architecture best practices
Incident response automation: A definitive guide to SOAR, AI, and faster MTTR
This article was originally published in June 2023 by Elisa Silverman. The most recent update was in May 2026.
