Platform
One platform for the complete AI governance engineering lifecycle.
Uraikkal connects application intelligence, governance decisions, data-risk controls, vendor policy engineering, implementation planning, testing and documentation — seven modules that carry a decision from raw app risk all the way to proven, documented enforcement.
- 01AI Trust Center
- 02AI Governance
- 03Control Matrix
- 04Policy Blueprint
- 05Vendor Workspace
- 06Deployment & Testing
- 07Deliverables
Every module contributes to one outcome: govern AI applications, and put those decisions into operation. For the same work in the order a team actually moves through it, see How It Works.
Module 01
AI Trust Center
Evaluate AI applications using DLP capabilities, enterprise controls, compliance relevance, and available security evidence.
Produces
- Trust score and risk rating
- AI category
- DLP activity coverage
- Governance recommendation
- Evaluation evidence
Outcome
Know which applications require approval, conditions, restriction, or prohibition.
Module 02
AI Governance
Classify each application into a governance category — Approved & Supported, Approved with Conditions, Restricted / Unassessed, or Prohibited — and scope that decision to the business units, groups, and application instances it applies to.
Produces
- Governance category per app
- Organisational scope and instances
- Decision rationale and owner
- Review and approval status
- AI Application Governance Register
Outcome
Move from a raw app list to a business-approved governance register.
Acme Corp — AI Governance
- Medium Risk
Azure AI Foundry
AI Analytics
- Medium Risk
GitHub Copilot
AI Code Assistant
- Medium Risk
Microsoft Copilot (M365)
Enterprise AI Assistant
Module 03
Control Matrix
Map data risk families against governance categories and activities to decide the exact DLP action for every combination.
Produces
- App access posture by category
- Data risk actions by category
- Prompt / upload controls
- Coaching message assignments
Outcome
Define exactly what should happen when sensitive data meets each AI app category.
Acme Corp — Control Matrix
| Risk family | Approved | Conditional | Restricted |
|---|---|---|---|
| Credentials, Keys & Secrets | Block | Block | Block |
| Regulated Data | Monitor | Coach + Just. | Block |
| Source Code | Allow | Monitor | Coach + Ack. |
| Intellectual Property | Monitor | Coach + Just. | Block |
Approved = Approved & Supported · Conditional = Approved with Conditions · Restricted = Restricted / Unassessed
Module 04
Policy Blueprint
Convert every Control Matrix decision into a vendor-neutral policy blueprint — intent, source and destination logic, and expected action.
Produces
- Policy intent and grouping
- Source / destination logic
- Data profile requirements
- Expected actions and priority
Outcome
Hold a policy model that survives a change of security vendor, because it describes intent rather than configuration.
Acme Corp — Policy Blueprints
- Block secrets everywhereBlock
activities: upload · prompt
- Protect regulated dataCoach
activities: upload
- Coach on customer dataCoach
activities: prompt
- Alert on source code uploadsAlert
activities: upload
Module 05
Vendor Workspace
Translate the neutral policy model into implementation-ready recommendations for the security platform you actually run.
Produces
- Recommended policy stack and order
- DLP profiles and app tags
- Required implementation objects
- Known limitations and validation checks
Outcome
Turn your control matrix into Netskope-ready policies without starting from a blank console.
Acme Corp — Netskope tenant
- P100Prohibited Applications — Access Blockblock
- P200Secrets & Keys — Global Blockblock
- P210Scoped — Corporate Copilot Tenant (Finance)protect
- P295AI Acceptable Use Reminder — Approved & Supportedcoach
- P300Approved & Supported — Content Protectioncoach
Module 06
Deployment & Testing
Prepare the implementation sequence and the proof that it worked — every dependency a policy needs before it goes live, and every scenario that must pass afterwards.
Produces
- DLP profile and object checklist
- Configuration sequence and prerequisites
- Must-pass and good-to-verify scenarios
- Expected vs. actual outcomes
- Tester, date, and evidence capture
Outcome
Give engineers a practical deployment path and prove every control works before the project is called complete.
Acme Corp — deployment tracker
- Create DLP Profiles43/43
- Confirm App Objects5/5
- Verify User Identity3/3
- Notification Templates8/12
- Validation Checks2/4
Acme Corp — Testing Plan
Sample records
- DLP-001Navigate to a prohibited GenAI app from a test account○ Pending
- DLP-002Upload a file containing Credentials, Keys & Secrets to any GenAI app✓ Passed
- DLP-003Paste an API key into an approved AI chat prompt✓ Passed
- DLP-004Upload a file containing Source Code to Generative AI✓ Passed
- DLP-005Upload a .pem file to a Restricted / Unassessed GenAI app✓ Passed
Module 07
Deliverables
Turn the same governance workflow into export-ready documents for every stakeholder — engineer, architect, auditor, and leadership.
Produces
- High & Low Level Design
- AI Application Governance Register
- Netskope Policy Pack
- Evidence Report
- Executive and posture reports
Outcome
Export the documents your engineers, architects, auditors, and leaders need.
- 01Executive Summary
- 02Governance Model & App Categories
- 03Policy Architecture
- 04Coaching & User Experience
- 05Risks & Mitigations
- 06Rollout Strategy
Reference
The vocabulary the platform runs on.
App risk ratings
Governance categories
DLP actions
Data risk families
- 01Credentials, Keys & Secrets
- 02Regulated Data
- 03Source Code
- 04Intellectual Property
- 05Security & Infrastructure Data
- 06Customer & Employee Data
- 07Financial & Commercial Data
- 08Legal & Contractual Data
- 09Business Operations & Internal Data
- 10Public & Low-Risk Data
Control intent → Netskope implementation
Netskope has no “Coach” action — coaching is a User Alert plus a notification template, and the acknowledge-vs-justify distinction lives entirely in which template is attached. This is exactly what gets built in the console.
- BlockBlock
- AlertAlert
- Coach + AcknowledgeUser Alert + notification template
- Coach + JustificationUser Alert + justification-required template
- Monitor / AllowAllow
FAQ
Questions we hear from security teams.
Does Uraikkal replace Netskope?
No. Uraikkal helps design, document, deploy, and validate AI governance controls. Netskope is the first supported vendor policy pack — enforcement stays with the platform you already run.
Does Uraikkal push policies directly into Netskope?
Not today. Uraikkal generates reviewable policy guidance, required objects, deployment checklists, testing plans, and deliverables. Every change to your tenant stays under your control.
Do I need to provide production data?
No. Uraikkal can work with governance inputs, app categories, risk families, and sanitized implementation context.
What vendors are supported?
Netskope is available now. Additional DLP, CASB and SSE platforms are planned and will be introduced through vendor-specific implementation packs.
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.
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.
Is this a one-time project or something we use continuously?
Both. Most teams start with an initial engagement — assessment through deployment — then keep using Uraikkal as apps, regulations, and the Netskope tenant change, so policies, evidence, and documentation stay current instead of going stale after the first rollout.
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 do you keep GDPR and HIPAA mappings accurate?
Regulation mappings, article references, and fine exposure figures come from a maintained compliance database, not from AI generation. The database updates independently as guidance changes — accuracy is a data problem, not a chatbot problem.
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.
Do we need to hire a dedicated engineer to run this?
No. Uraikkal is built for the DLP, CASB, or security architect you already have — it structures the decisions and generates the artifacts an experienced practitioner would otherwise build by hand. It doesn't require a new full-time role.
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.
Walk the platform yourself.
Explore a preconfigured workspace in read-only mode — from AI Trust Center to evidence report, at your own pace.