The promise of AI-driven financial insights depends on one critical factor: access to high-quality, diverse datasets. But in the financial services industry, data access isn't just about connectivity—it's about compliance, security, and fine-grained control over who sees what, when, and how much.
CloudQuant's Data Liberator platform solves this paradox through its sophisticated entitlements service engine, providing AI teams with streamlined access to financial datasets while maintaining the granular controls that enterprise security and compliance teams demand.
The AI Data Access Challenge
Modern AI models—whether for market prediction, risk assessment, or algorithmic trading—require training on diverse historical datasets. Data teams need to:
Traditional approaches force teams to choose between two undesirable extremes:
How Liberator's Entitlements Engine Works
Liberator's entitlements service provides a middle path: programmatic access with policy-based controls. Here's how it transforms AI workflows:
RESTful API with Built-in Authorization
Every API request to Liberator passes through the entitlements engine. Data teams authenticate once using OIDC, and their token carries their entitlements:
bash
curl -H Content-Type:application/json -d '{"name":"dataset","user":"data-scientist","token":"your-token-here"}' https://liberator.cloudquant.ai/liberator/query
Behind the scenes, the entitlements engine validates:
Dataset-Level and Symbol-Level Controls
Unlike monolithic data platforms, Liberator supports entitlements at multiple granularities:
Dataset-level: Grant access to entire datasets like "US Equities" or "Options Data"
Symbol-level: Restrict access to specific tickers or asset classes—perfect for teams working with proprietary watchlists or sector-specific models
Field-level: Control which columns are visible (price, volume, bid/ask spreads, etc.)
This means your equity research team can access large-cap constituents while your options desk sees only their authorized derivatives universe—all from the same platform.
Time-Based Access Patterns
AI models need historical data, but not all users need real-time access. Liberator's entitlements engine supports:
This aligns data access with both business needs and licensing constraints from upstream providers.
Audit Trails for Compliance
Every query generates detailed audit logs:
For SOC2-compliant organizations (like CloudQuant), these logs feed directly into security information and event management (SIEM) systems, providing the paper trail auditors require.
Real-World AI Workflow: Model Training with Liberator
1. Discovery Phase
python
import liberator
# List available datasets the user is entitled to, with schema details
datasets = liberator.datasets(
entitled=True,
details=True,
schema=True,
user="data_scientist",
token="your-token-here"
)
Outcome: The data scientist sees only datasets they're entitled to access—no time wasted exploring restricted data.
2. Historical Data Retrieval
python
import liberator
# Query 3 years of daily data for large-cap equities
liberator.url = 'https://liberator.cloudquant.com'
df = liberator.get_dataframe(
liberator.query(
name="equity_market_data",
symbols="TICKER_A,TICKER_B,TICKER_C,TICKER_D",
back_to="2021-01-01",
as_of="2023-12-31",
fields="close,volume,adj_close",
user="data_scientist",
token="your-token-here"
)
)
Outcome: The entitlements engine validates the request in milliseconds. If approved, data streams directly into the model training pipeline.
3. Iterative Experimentation
As the model evolves, the team needs additional data:
python
# Add correlation with sector ETFs
sector_df = liberator.get_dataframe(
liberator.query(
name="equity_market_data",
symbols="ETF_SECTOR_A,ETF_SECTOR_B,ETF_SECTOR_C",
back_to="2021-01-01",
as_of="2023-12-31",
user="data_scientist",
token="your-token-here"
)
)
Outcome: No need to file tickets or wait for manual approvals—if the user is entitled to the requested symbols, they get instant access.
4. Out-of-Sample Validation
For model validation, the team queries recent data:
python
# Get recent data for backtesting (last 250 records per symbol)
validation_df = liberator.get_dataframe(
liberator.query(
name="equity_market_data",
symbols="TICKER_A,TICKER_B,TICKER_C,TICKER_D,ETF_SECTOR_A,ETF_SECTOR_B,ETF_SECTOR_C",
back_to="2024-01-01",
record_limit=-250,
user="data_scientist",
token="your-token-here"
)
)
Outcome: The entitlements engine ensures the user can only access data within their subscription's recency limits.
The Enterprise Security Advantage
While data scientists experience seamless access, the security team maintains complete control:
Beyond Traditional Data Lakes
Traditional data lakes require AI teams to:
Liberator's entitlements-driven approach flips this model:
Real-World Impact
CloudQuant's Data Liberator platform, powered by its entitlements service engine, enables financial institutions to:
Getting Started
CloudQuant Data Liberator gives your data teams instant, governed access to institutional-grade financial data—without the tickets, the wait, or the compliance risk. Secure by default. Auditable by design. Ready when your team is.
Stop building access infrastructure. Start building better models.
Contact us to see it in action.