Cato AI Security
Your AI policy describes an organisation you do not have — Cato AI Security sees which AI services your staff actually reach — because AI traffic crosses the same PoP as everything else, with no new agent to deploy.
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The Cato SASE platform family
This page covers AI Security — shadow AI and agents. The rest of the platform:
Quick facts
30-second orientation- Product
- AI Security — shadow AI, apps and agents
- Why it works here
- AI traffic is traffic the platform already sees
- The first output
- An inventory of what staff actually use
- The newest module
- Ask what is GA today versus roadmap
- No new agent
- It rides the inspection you already have
- The hard part
- Deciding policy, not seeing the traffic
- Pricing
- Quote-only — newest module, confirm packaging
- In India via
- TechBag — INR/GST, scoping and support
Understand AI governance before you buy it
Most product pages skip this. We start here — so you buy a capability, not a buzzword.
What is Cato AI Security?
Visibility and control over AI use, from traffic you already inspect — shadow AI, sanctioned applications and AI agents, with no new endpoint agent to deploy.
A policy describing an assumption vs an inventory — the honest table
What consolidation actually replaces, dimension by dimension.
| Dimension | An acceptable-use policy | AI Security (Cato) |
|---|---|---|
| Visibility | A policy describing an assumption | An inventory of what is actually used |
| Deployment | A new agent or proxy | None — it rides existing inspection |
| The control | An application allow-list | Application control plus prompt data policy |
| SaaS-embedded AI | Invisible | Seen, because it is still traffic |
| Investigation | A separate tool | The same data lake as network events |
| What it is NOT | — | Not a view of anything that never leaves the laptop |
This is the NEWEST of the four modules: ask what is GA today versus preview, especially for AI agents. And it is network-based, so AI use that never leaves the laptop is outside its view.
The four pieces of the platform
Vendors love diagrams; buyers need to know what they’re actually operating. Here’s the whole platform, demystified.
Discovery of AI use
First, identify which AI services are actually used across the estate, sanctioned or not — a policy written against an assumed list misses reality.
Application control
Deciding which AI applications are permitted and under what conditions. The technical part is straightforward; the organisational part — deciding what to allow — is where the time actually goes.
Data controls
The real risk in most estates is not the AI service itself but what staff paste into it. Policy on what data may move into an AI application is where this module meets DLP.
AI agents
AI agents running multi-step workflows are a new traffic class with credentials and no one watching each step. The least settled area — ask what exists today.
One view of AI use at the network edge — services discovered, apps allowed by policy and sensitive data kept out of prompts.
Six capabilities. Discover, decide, control.
Cato AI Security governs the AI already in your traffic — discovery, control and the rest of the Cato Networks portfolio.
Find what nobody registered
Which AI services are actually reached from your network — almost always more than the sanctioned list.
It rides existing inspection
AI traffic crosses the same PoP as everything else, so visibility needs no new endpoint agent or integration.
Sanction, block, constrain
Which AI applications are permitted, for whom and when. Enforcement is simple; agreeing the policy takes time.
What staff paste in
The real exposure is usually prompt content, not the service, so policy on data entering AI apps meets DLP.
Software with credentials
AI agents running workflows are a new traffic class. The least settled area — ask what ships today.
In the same data lake
AI events sit beside network and security data, so an investigation reads one place, not several.
Watch Cato in action
AI security in the platform, securing AI apps and agents, and agentic threats.
Cato AI Security: Built for How AI Works
Where AI traffic meets the security stack.
Secure Enterprise AI Apps and Agents
Visibility and governance over AI use.
Defending Against the Next Generation of Agentic Attacks
How agentic attacks differ from what came before.
Want a live, India-context walkthrough for your environment?
Book a guided demo →A policy states intent. An inventory states fact.
Here’s what genuinely sets it apart — and exactly where it stops.
AI traffic is traffic, which is why this belongs here
Most AI governance needs something new deployed — an agent, browser extension or proxy. A platform already inspecting all outbound traffic sees AI requests like any other, which is why AI security lands in SASE. The boundary: it only sees AI use that crosses the network, not something running locally on a laptop.
The inventory arrives before the policy, and it should
Teams write an AI policy, then find it covers a fraction of real use: browser tabs, AI features quietly added to existing SaaS, a tool a team found last month. Discovery produces the real, longer list. So inventory first, then decide per service whether to sanction, constrain or block, then enforce.
The risk is usually the prompt, not the service
The bigger AI risk is not which services are allowed but what staff paste into them — customer records, unreleased financials, source code, DPDP personal data. Controlling reachable apps is the easy half; controlling data going in is the half that matters and overlaps DLP. Ask how prompt content is inspected.
This is the newest module — scope it accordingly
AI Security arrived after SD-WAN, SSE and ZTNA, in a fast-moving category. The logic and fit are sound, but it is the module most likely to be shown partly on roadmap, and agent capability is unsettled for every vendor. Ask what is generally available, in preview and planned, and record the difference.
The numbers behind the platform
What your AI governance rollout looks like
Admit you do not know
Most organisations cannot list the AI services in use. Starting from that honesty makes the discovery output useful rather than embarrassing.
Run discovery, enforce nothing
See what is actually used, including AI features inside SaaS tools nobody classified as AI. The inventory is the first real deliverable.
Decide per application
Sanction, constrain or block, with the business in the room. This conversation takes longer than the configuration and involves more people.
Write policy for prompts, not just apps
The exposure is usually what staff paste in. Application allow-listing alone leaves the actual risk untouched.
Separate GA from roadmap
Especially for agent-related capability. Record what you can turn on today so the evaluation reflects the product rather than the demo.
Re-run discovery
New AI services and SaaS AI features appear constantly, so a one-time inventory is stale within a quarter.
The review scoreboard
Modelled on Gartner Peer Insights structure. *Counts and breakdowns are illustrative pending verified review collection.
Quick poll — what’s driving your evaluation?
Talk to an advisor“Discovery found AI features inside SaaS tools we already paid for. Nobody had classified those as AI, and that was the most useful thing it told us.”
“No new agent to deploy was the deciding factor. Every other option meant an endpoint rollout we did not have the appetite for.”
“Ask what ships today. Some of what we saw demonstrated was clearly ahead of what we could turn on, particularly around agents.”
“The technical control was the easy part. Agreeing internally which AI tools to allow took three months and involved people who had never met.”
Where everyone sits — the grids
Analyst firms bury this view behind paywalls, and review-site grids score satisfaction, not fit for your estate. So here’s TechBag’s synthesis of the AI security market — tap any vendor to see why it sits where it does.
TechBag AI Security Grid
Execution strength vs product vision — the classic market map, minus the paywall.
Rides existing inspection; no new agent.
Prompt-Level Control × Zero-Deploy Visibility
The grid nobody publishes — depth of prompt-level control vs how much visibility you get without deploying anything.
Inside the platform that already sees it.
Positions are TechBag’s illustrative synthesis of public review-platform data and vendor documentation — not a reproduction of any analyst graphic. Verify before relying on it.
AI Security vs the alternatives
Against an AI-security point tool, an acceptable-use policy, and nothing — on discovery, deployment effort and what it cannot see.
| Dimension | Cato AI Security | An AI-security point tool | An acceptable-use policy | Nothing |
|---|---|---|---|---|
| Discovery of actual use | From existing traffic | Usually good | None | None |
| Deployment effort | None new | An endpoint rollout | A document | None |
| SaaS-embedded AI | Visible | Varies | No | No |
| Prompt data control | Meets DLP | Often a strength | No | No |
| Agent-era maturity | Newest area | Also emerging | No | No |
| Off-network use | Not visible | Agent may see it | No | No |
Which approach fits you?
Honest fit signals — because the fastest way to lose your trust is to pretend one product wins every scenario.
Choose Cato AI Security if…
- You genuinely do not know which AI services your staff use today
- An endpoint agent rollout is the objection blocking every other option
- Cato already inspects your traffic, so this is visibility you have half-bought
- Prompt content, not the service list, is what worries your risk function
Compare a point tool if…
- You need visibility into AI use that never crosses the corporate network
- Deep prompt-level controls are the entire requirement rather than one part
- Your network is not on Cato, which removes the no-new-agent advantage
Do not expect…
- Visibility into anything that never leaves the endpoint — it is network-based
- Agent-era capability to be settled; ask what is GA today, of any vendor
- The policy decisions to be easy — agreeing what to allow takes longer than the setup
Cato AI Security is one of 23 sase & sse products TechBag carries. The SASE & SSE guide narrows them to a shortlist and shows the reasoning. →
What does unmonitored AI use cost you?
Drag the sliders (users; estimated cost of a data exposure incident). Estimates model unmonitored AI use and the data staff paste into prompts without oversight. Illustrative.
Loaded cost = salary + overheads per productive hour. Illustrative only — your TechBag quote models your actual environment and modules.
Three ways to consume it
Quote-only — no published price. As the newest module, confirm how it is packaged against the other three. TechBag scopes it and quotes in INR with GST.
AI Security
Best when you cannot list your AI use
- Shadow AI discovery from existing traffic
- No new endpoint agent to deploy
- Application control and prompt policy
+ Platform add-ons
Best for a broader rollout
- Scoped to your estate
- Add-on modules as needed
- Phased, right-sized deployment
+ the wider platform
Best with SSE already running
- Rides inspection you already have
- AI events in the same data lake
- Prompt policy meets existing DLP
Buy it for less — TechBag pricing beats list
Whatever the list prices above, TechBag negotiates a significantly better deal — with GST-compliant INR invoicing and local support. Ask us for your discounted quote.
Get an India-ready quote
Tell us your requirements and current tools — we’ll model it against what you spend today.
The 8 questions to ask every vendor
Take this into your next vendor call — including ours.
Can you list the AI services in use across your organisation today? If that answer is an estimate, discovery will surprise you.
Have you counted AI features inside tools you already own? Most inventories miss these entirely.
How is prompt content inspected, and what policy can apply to it? An application allow-list does not address the main exposure.
What ships today, what is in preview, what is planned — particularly for AI agents? This is the newest module.
Do you need visibility into AI use that never crosses the network? If so, this is network-based and will not see it.
Who decides which AI tools are permitted? That conversation takes longer than the technical work and needs the business present.
Could personal data reach an AI service through a prompt? That is a DPDP question before it is a security one.
How is the newest module priced relative to the other three? Confirm rather than assuming it is included.
Questions buyers ask
Ready to evaluate Cato AI Security?
Run discovery before writing any policy — the inventory is reliably longer than the sanctioned list — or let a TechBag advisor separate what is generally available today from what is roadmap.
Stats, ratings, review counts and pricing are illustrative and sourced from public materials; verify before purchase.