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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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How it’s rated
Full scoreboard ↓Quick answer
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 five 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
Identifying which AI services are being used across the estate, sanctioned or not. This comes first because a policy written against an assumed list governs a different organisation from the one you have.
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
Agents initiating multi-step workflows are becoming their own traffic class, with credentials and reach but no person watching each step. Newest and least settled area — ask precisely what exists today.
One telemetry fabric across endpoint, cloud, and network — threats correlated once, not chased console to console.
Six capabilities. Discover, decide, control.
Cato AI Security governs the AI already in your traffic — discovery, control and the portfolio, and paired with the human firewall.
Find what nobody registered
Which AI services are actually reached from your network. Almost always more than the sanctioned list, and the inventory arrives before any policy is written.
It rides existing inspection
Because AI traffic passes through the same PoP as everything else, visibility needs no new endpoint agent or integration. That is the structural reason this belongs inside SASE.
Sanction, block, constrain
Which AI applications are permitted, for whom, under what conditions. The technical enforcement is simple; agreeing the policy internally is the part that takes time.
What staff paste in
The exposure in most estates is the content of prompts, not the service itself. Policy on what data may move into an AI application is where this meets DLP.
Software with credentials
AI agents initiating workflows are a new traffic class with reach and no person watching each step. The least settled area — ask what ships today rather than assuming.
In the same data lake
AI events land beside network and security data, so an investigation reads one place. That context is the argument for AI security inside the platform rather than beside it.
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 approaches to governing AI use require deploying something new: an endpoint agent, a browser extension, a proxy in front of specific services. A platform that already inspects all outbound traffic does not need any of that, because a request to an AI service looks like every other request it is already examining. That is the structural reason AI security keeps appearing inside SASE platforms rather than as a separate category — the visibility is a by-product of inspection that exists anyway. It also sets the boundary honestly: this sees AI use that crosses the network. Something running entirely on a laptop without network egress is outside its view, as it would be for any network-based control.
The inventory arrives before the policy, and it should
Organisations typically approach AI governance by writing a policy, and then discover the policy describes a fraction of what is happening. AI services reach staff through browser tabs, through features quietly added to SaaS tools they already use, and through a team that found something useful last month. Discovery produces the actual list, and it is reliably longer than expected. The practical sequence is therefore inventory first, decisions second: see what is genuinely in use, decide for each whether to sanction, constrain or block, and only then enforce. Writing policy against an imagined estate produces a document that governs nobody and a compliance position that evaporates on first examination.
The risk is usually the prompt, not the service
Discussion of AI risk tends to focus on which services are permitted, and that framing misses where most exposure actually sits. A sanctioned, enterprise-grade AI service used carelessly leaks more than an unsanctioned one used with judgement, because the material question is what staff paste into it — customer records, unreleased financials, source code, personal data that carries obligations under DPDP. Controlling which applications are reachable is the easier half; controlling what data moves into them is the half that matters and the half that overlaps with DLP. When scoping, ask specifically how prompt content is inspected and what policy can be applied to it, rather than accepting an application allow-list as the answer.
This is the newest module — scope it accordingly
AI Security arrived well after SD-WAN, SSE and ZTNA, and in a category moving as quickly as this one that matters. The underlying logic is sound and the architectural fit is genuine, but it is also the module most likely to be presented partly on roadmap, and agent-related capability in particular is the least settled area across every vendor in this market rather than at Cato specifically. The right diligence is unglamorous: ask what is generally available today, what is in preview, and what is planned, and get that distinction into your evaluation notes rather than taking a demo as a description of the shipped product. A vendor confident in what they have will draw those lines clearly.
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 appear constantly, and existing SaaS tools add AI features in release notes nobody reads. A one-time inventory is out of date 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 G2 retired its Grid. 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.
Detection × Portfolio Integration
The grid nobody publishes — how much visibility you get without deploying anything vs depth of prompt-level control.
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 cybersecurity 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
AI Security is one of 20 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.