Turn AI governance decisions into deployment-ready security controls.
Most AI governance stops at the decision. Uraikkal carries it through to vendor-ready DLP policies, deployment plans, and the test evidence that proves they work.
Built for CISOs, security architects, DLP teams and AI governance leaders.
AI Trust Center
179 applications catalogued · 176 fully evaluated · 3 awaiting evaluation
- Low Risk
Azure AI Foundry
Microsoft · AI Data Analysis
Trust Score
88/100
DLP Activities
7/7
Classification: Approved & Supported
- Medium Risk
GitHub Copilot
Microsoft · AI Coding Assistants
Trust Score
82/100
DLP Activities
7/7
Classification: Approved with Conditions
- Critical Risk
DeepSeek
DeepSeek AI · AI Chatbots
Trust Score
33/100
DLP Activities
7/7
Classification: Prohibited
- Medium Risk
Adobe Firefly
Adobe · AI Creative & Design
Trust Score
80/100
DLP Activities
7/7
Classification: Restricted / Unassessed
- High Risk
Adobe Express
Adobe Inc. · AI Creative & Design
Trust Score
67/100
DLP Activities
7/7
Classification: Restricted / Unassessed
- Critical Risk
Ada
Ada Support Inc. · AI Customer Support
Trust Score
43/100
DLP Activities
0/7
Classification: Restricted / Unassessed
- 1. AI Trust Center
- 2. AI Governance
- 3. Open the app
- 4. Control Matrix
- 5. Policy Blueprints
Showing step 1 of 5: AI Trust Center
See the full AI Trust Center sample →The problem
AI governance often stops before the real work begins.
Your organisation may have an AI policy, an application inventory and a governance committee. But security teams are still expected to manually answer the difficult implementation questions.
- Which AI applications should be approved, restricted or prohibited?
- What data can be shared with each category?
- Which controls should alert, coach, justify or block?
- How should those decisions be implemented in Netskope or another security platform?
- What objects, dependencies and policy order are required?
- How will the implementation be tested and evidenced?
- What documentation will architecture, risk and audit teams expect?
Governance is not complete until the controls can be implemented, validated and maintained.
How it works
From AI discovery to validated controls.
Seven steps that carry a decision from raw application risk to proven, documented enforcement — and back around as your AI estate changes.
01
Assess
Understand the applications entering your organisation
Outcome
A consistent foundation for application decisions.
02
Govern
Decide how each AI application should be used
Outcome
An approved AI application governance register.
03
Design
Define the controls that apply to each risk
Outcome
A defensible, risk-based control model.
04
Engineer
Convert governance into vendor-ready policy architecture
Outcome
A deployment-ready vendor policy pack.
05
Deploy
Give engineers a structured implementation plan
Outcome
A practical implementation guide for the security engineering team.
06
Validate
Prove that the controls work as intended
Outcome
Structured test results and implementation evidence.
07
Operate
Maintain governance as AI usage changes
Outcome
Continuous AI governance rather than a one-time project.
Deliverables
Every project produces implementation-ready deliverables.
Uraikkal does not stop at recommendations. It produces the technical, governance and validation artifacts required to move the initiative forward.
Where Uraikkal fits
More than AI discovery. More than policy documentation.
Uraikkal combines the repeatability of software with the structured outcomes of a specialist consulting engagement.
| Capability | AI Governance / GRC platforms | Runtime AI Security & SSE | Consulting | Uraikkal |
|---|---|---|---|---|
| AI application visibility | Yes | Yes | Point-in-time | Yes |
| Risk-based control design | Templates | Enforces, doesn't design | Yes | Yes |
| Vendor-specific policy architecture | Not covered | Own console only | Yes | Yes |
| Deployment checklist | Not covered | Not covered | Yes | Yes |
| Structured testing plan | Not covered | Not covered | Yes | Yes |
Five of eleven capabilities shown — the full table, including where each alternative is the better choice, is on the comparison page.
Uraikkal does not replace your enforcement platform. It turns governance decisions into the architecture, objects, policies, deployment steps and evidence your existing platform requires.
Vendor support
Built for real-world security implementation.
Uraikkal converts vendor-neutral AI governance and control decisions into implementation-ready recommendations for Netskope today.
Available now
Netskope
- Prohibited AI application controls
- Critical-data and secrets protection
- Approved application policies
- Conditionally approved application controls
- Restricted and unassessed application fallbacks
- Application-instance controls
- Group-based entitlements
- DLP profiles and notification requirements
- Policy ordering and dependency plans
- Testing and acceptance criteria
Uraikkal produces reviewable policy guidance and required objects. It does not push configuration into your tenant.
Planned — not available today
Additional platforms
- Microsoft Purview
- Symantec DLP
- Forcepoint
- Additional DLP, CASB and SSE platforms
Introduced through vendor-specific implementation packs. No delivery date is committed here.
Who it's for
Designed for teams responsible for making AI governance real.
Security Leaders
Visibility into AI governance posture, implementation readiness, open risks and the decisions still waiting on someone.
Security Architects
Defensible control architectures with documented assumptions, dependencies, alternatives and limitations.
DLP and CASB Engineers
Governance decisions translated into policy structures, DLP profiles, configuration objects and testable controls.
AI Governance and Risk Teams
Application decisions, organisational scope, data-handling rules and evidence maintained across the governance lifecycle.
Consultants and Service Providers
Standardised delivery, less repetitive documentation, and consistent customer-facing artifacts across engagements.
Uraikkal is best suited to organisations that
- Have an active GenAI adoption or governance initiative
- Operate a mature DLP, CASB or SSE programme
- Need to translate governance into technical controls
- Work in regulated or data-sensitive industries
- Want a repeatable internal capability rather than isolated project work
Plans as stages
From governance decision to deployed security control.
Uraikkal's plans are named after how far they take you through the engagement, not a feature count — so “what plan am I on” and “how far through the process am I” are the same question. Governance, Blueprint and Architect are cumulative stages of one engagement, not separate feature bundles.
Governance
Decide
Assess AI applications, establish governance decisions, and maintain a defensible AI governance register.
Blueprint
Design
Translate AI governance decisions into structured, vendor-neutral DLP and data-protection controls.
Architect
Implement & Prove
From governance decisions through vendor implementation and validation — the complete Uraikkal workflow available to commercial customers today.
FAQ
Questions we hear often.
Who is Uraikkal for?
Security leaders, security architects, DLP and CASB engineers, AI governance and risk teams, and the consultants and service providers who deliver these programmes.
How long before we have usable policies, not just a report?
Governance decisions and a prioritised policy set are typically ready within the first working sessions. Deployment checklists, testing plans, and evidence reporting follow in the same engagement — you're not waiting on a separate phase to get something you can act on.
How does this compare to hiring a DLP consultant?
A consultant produces a point-in-time deliverable. Uraikkal produces the same class of artifact — governance decisions, policy architecture, deployment plans, test evidence — as a repeatable output you can regenerate as your environment changes, without re-commissioning a new engagement each time.
Can I trust the Netskope configuration steps Uraikkal generates?
Configuration steps come from a structured, version-controlled implementation database — never generated freeform by AI. Claude is used only to explain and contextualise; when it's uncertain, it says so explicitly rather than guessing.
How is our data isolated from other customers?
Every table in the platform is scoped to your organisation from day one and enforced with row-level security at the database layer, not just filtered in application code. Nothing you upload or generate is visible across organisations.
Not ready to talk to anyone?
Use the free DLP regex tester instead — 50 detection patterns for cards, credentials, PII and PHI, with sample data for each. No sign-up, and nothing you type leaves your browser.
Your AI governance programme should produce more than recommendations.
Explore a completed sample workspace — real governance decisions, policy architecture, deployment plans and evidence — self-guided, read-only, and yours to review at your own pace.