What are AI security tools?
By Identra · Updated
AI security tools discover, assess, test or control risks in AI applications, models, agents and employee AI use. Their value depends on which data flows and actions they actually cover, and whether they only report a risk or stop it.
What do AI security tools protect?
Mostly four things. Employees typing into ChatGPT or Gemini in a browser. Assistants like Microsoft 365 Copilot that can read SharePoint and Outlook. Coding agents such as Claude Code and Cursor running on laptops. And whatever your own engineers build on model APIs.
Each one goes wrong in its own way. A contract uploaded to a personal ChatGPT account is a data problem. An agent deleting a cloud storage bucket comes down to the credentials it was handed. An app that loads an untrusted model file is closer to a supply chain issue, and needs a different tool again.
Before shopping, list the workflows you actually run, including shadow AI. Note the owner, account, reachable data and possible actions for each.
One scoping note. This page is about security for AI. Products that use AI to triage alerts are a separate category, though some vendors sell both.
What are the main types of AI security tools?
Vendors blur these labels and one product often spans several rows. Use the table to know what to ask. Then check exact scope app by app.
AI usage control
- What it does
- Finds AI use and applies access policy
- Check separately
- Which apps, account types, devices and actions
AI security posture management
- What it does
- Assesses AI resources, configs and permissions
- Check separately
- Whether a finding can trigger a fix or a block
AI data loss prevention
- What it does
- Inspects data headed into AI for sensitive content
- Check separately
- Prompts, uploads, responses and connector reads
AI gateways and firewalls
- What it does
- Apply policy to AI traffic routed through them
- Check separately
- Bypass paths and what downstream tools may do
AI red teaming
- What it does
- Probes models and workflows for abuse
- Check separately
- Fixes and retests after changes
Agent access controls
- What it does
- Limit agent permissions and authorize actions
- Check separately
- Revocation and enforcement at execution
Model artifact scanning
- What it does
- Inspects model files for unsafe content
- Check separately
- Supported formats and what a scan can't prove
Where does each control actually sit?
Placement decides coverage. A browser extension sees what employees do in Chrome or Edge. An endpoint agent can see desktop apps, coding agents and the MCP servers configured in files like ~/.cursor/mcp.json. Integrations with Okta, Entra ID or Google Workspace show which third-party apps hold OAuth grants. An AI gateway only sees traffic someone routed through it.
AI security posture management tells you about exposure. A finding doesn't block anything. Ask which piece stops the action before it completes.
Agents add a wrinkle. Inspecting content and authorizing actions are different jobs, and a harmless-looking request can still call a destructive tool. The OWASP AI Agent Security Cheat Sheet covers enforcing tool permissions outside the model.
What does that look like for one team?
Say a finance analyst signs into a personal ChatGPT account and uploads a supplier contract with bank details on page four. Down the hall, a connected assistant has read access to the contracts library and permission to send email.
Account policy deals with the personal login. AI data loss prevention can catch the bank details if the file goes somewhere else. The assistant's send permission is a separate finding, and only a permission review surfaces it. Proving one control works tells you nothing about the others.
Then try the nasty case. Plant a synthetic contract that tells the assistant to email its contents to an outside address. That's indirect prompt injection. Use a test mailbox and check whether mail actually left. The assistant's last message is not evidence.
What should you test before buying?
Run your real workflows in the trial. Include normal work, so you see false blocks, and prohibited work, so you see misses. For every test, write down what the product saw, which policy fired, whether the action completed and what you could investigate afterward.
- Coverage. The apps, OS versions, account types and deployment paths you actually run.
- Enforcement. A block before completion and an alert an hour later are very different products.
- Bypass. Direct API calls and alternate clients, inside an authorized test.
- Failure mode. What happens when the inspection service or an integration goes down.
- Data handling. Where content is processed, how long logs live, who can read them.
- Response. Whether revoking access really takes effect in the target service.
What requirements should you write first?
Which accounts may use which app. Which data may go in. Which actions an agent may take on its own. Each policy needs an owner, an exception process and a tested way to revoke access.
Retest when models, tools, permissions or integrations change. AI red teaming is the stress test for the whole stack once it's assembled, and a passing demo only covers the conditions you tested.
How Identra thinks about it
Identra covers AI use in three places: the browser, macOS and Windows endpoints, and connected identity, SaaS and cloud providers. Teams can allow, redirect or block supported browser AI apps by signed-in account, check prompts on the device before they're sent, check agent tool calls against policy and review a record of every endpoint agent run.
Go deeper: AI security, built on identity
Frequently asked questions
Can existing security tools cover AI risks?
Partly. Identity controls, endpoint protection, DLP and logging all handle pieces of it. Test them against your AI accounts, data flows and agent actions before buying something new.
Can an AI firewall stop all prompt injection?
No tool guarantees that. Pair inspection with narrow permissions, authorization outside the model and tests on workflows that read untrusted content.
Does AI security posture management block unsafe actions?
Not by itself. Posture tools find exposure and misconfiguration. Blocking takes an enforcement capability, and you should confirm it exists for each action and environment you care about.
Do hosted model users need model artifact scanning?
Scanning needs the model files. If you only call a hosted API, focus on the provider's terms, data handling, your application's behavior and access controls.
Which AI security tools should an organization evaluate first?
Whatever covers the workflow with the most sensitive data or the most powerful permissions. For employee chatbot use that's account and data controls. Connected agents also need owners, permission reviews and action authorization.