Compare
Where Uraikkal fits — and where it doesn't.
Most teams solving AI governance are choosing between four things, not evaluating one. This page is the honest version of that comparison, including the cases where something else is the better buy.
At a glance
Capability comparison
The same table shown on the homepage, in full. “Limited” means the capability exists but stops short of producing something an engineer can implement from.
| Capability | AI Governance / GRC platforms | Runtime AI Security & SSE | Consulting | Uraikkal |
|---|---|---|---|---|
| AI application visibility | Yes | Yes | Point-in-time | Yes |
| Application risk assessment | Yes | Risk score only | Yes | Yes |
| Governance categorisation | Yes | Category tags | Yes | Yes |
| Risk-based control design | Templates | Enforces, doesn't design | Yes | Yes |
| Vendor-specific policy architecture | Not covered | Own console only | Yes | Yes |
| Required implementation objects | Not covered | Not covered | Yes | Yes |
| Deployment checklist | Not covered | Not covered | Yes | Yes |
| Structured testing plan | Not covered | Not covered | Yes | Yes |
| Evidence reporting | Yes | Raw logs | Limited | Yes |
| Repeatable after the initial project | Yes | Yes | New effort | Yes |
| Continuously reusable | Yes | Yes | Limited | Yes |
Uraikkal does not replace your enforcement platform. It turns governance decisions into the architecture, objects, policies, deployment steps and evidence your existing platform requires.
Option 01
An AI governance or GRC platform
What it does well
- Maintaining an AI application inventory and register
- Running assessment questionnaires and approval workflows
- Mapping decisions to frameworks and producing audit evidence
- Giving risk and compliance teams a system of record
Where the work is still yours
- The output is a decision, not a control — someone still has to design the enforcement
- No vendor-specific policy architecture: no objects, no policy order, no fallback behaviour
- No deployment sequence for the engineer who has to configure it
- No test plan proving the control actually behaves as the decision intended
Choose this instead when
Your problem is the register and the audit trail, and you already have engineers who can turn a governance decision into working policy without help.
Option 02
Your SSE / runtime AI security platform
What it does well
- Actually enforcing at runtime — this is the layer that blocks, coaches, and alerts
- Discovering shadow AI usage from real traffic
- Inline inspection of prompts, uploads, and responses
- Telemetry and incident data you can't get anywhere else
Where the work is still yours
- It enforces what you configure; it doesn't decide what should be configured
- Design lives in a specialist's head, not in a reviewable artifact
- Governance rationale isn't captured — you can't show an auditor why a control exists
- Every new AI application restarts the same manual design conversation
Choose this instead when
You need enforcement — and you should keep it. Uraikkal doesn't replace it. If you already have a DLP architect turning decisions into Netskope policy consistently and documenting it, you may not need the layer above it.
Option 03
A consulting engagement
What it does well
- Deep, senior expertise applied to your specific environment
- Producing exactly the artifacts you asked for — HLD, LLD, policy design, test plans
- Absorbing the work when you have no internal capacity
- Accountability: someone is contractually on the hook
Where the work is still yours
- The deliverable is point-in-time — accurate the week it ships, drifting from month two
- Regenerating it as your AI estate changes means re-commissioning the engagement
- The reasoning leaves when the consultant does
- Cost scales with every repeat, and the second engagement rarely costs less than the first
Choose this instead when
This is a one-off programme with a fixed end date, or the scope is unusual enough that it genuinely needs bespoke thinking rather than a repeatable model.
Option 04
Building it in-house
What it does well
- Fits your environment exactly, because you built it for your environment
- No vendor, no subscription, no procurement cycle
- Full control over the data model and how decisions are represented
Where the work is still yours
- The hard part isn't the spreadsheet — it's the maintained knowledge behind it: vendor capability mapping, regulation mapping, and what a control should be for a given risk
- It becomes one person's side project, and it stops the week they change roles
- Keeping vendor and regulatory mappings current is ongoing work nobody is funded to do
- Every artifact — HLD, test plan, evidence report — is written by hand, every time
Choose this instead when
You have a dedicated team with the mandate and time to own this permanently, and AI governance is core enough to your business to justify that as a standing investment.
The short version
Uraikkal is the layer between the decision and the console.
A GRC platform records that an application was approved. Your SSE platform enforces whatever someone configured. A consultant bridges the two once, by hand. Uraikkal is the repeatable version of that bridge: governance decisions in, vendor-ready policy architecture, deployment plans, test evidence and documentation out — regenerated as your AI estate changes rather than re-commissioned.
See the artifacts before you decide.
The fastest way to judge whether this replaces work you're currently doing by hand is to look at what it produces — a full sample policy pack, and a read-only workspace with a completed engagement in it.