Quantitative Research Workflows with AI Governance

Quantitative researchers use LLMs for literature review, code generation, statistical analysis, and hypothesis exploration. These AI interactions carry unique risks: leaking proprietary alpha signals, contaminating research with look-ahead bias, or exposing restricted datasets to external providers. Research workflows also produce intellectual property that must be tracked and audited.

Use this page when

Your quant researchers use LLMs for literature review, code generation, statistical analysis, or hypothesis exploration.
You must prevent proprietary alpha signals and live P&L data from reaching external AI providers.
You need content classification policies to distinguish between sharable research context and confidential IP.
Research workflows require attribution logging and audit trails for intellectual property tracking.

Keeptrusts enforces governance controls across the full quantitative research lifecycle.

Primary audience

Primary: Technical Leaders
Secondary: Technical Engineers, AI Agents

Research Governance Architecture

Research Environment (Jupyter / IDE)
  → kt gateway (port 41002)
    → Input policy chain (IP protection, data classification)
      → [Block / Escalate → 409]
      → Upstream LLM provider
    → Output policy chain (bias checks, attribution logging)
    → Response to researcher
  Side-effects:
    └─ Decision event → POST /v1/events → audit log

Research Notebook Governance

Notebook Content Classification

Classify research notebook content before it reaches AI providers:

pack:
  name: quantitative-research-rules-1
  version: 1.0.0
  enabled: true
policies:
  chain:
  - safety-filter
policy:
  safety-filter:
    block_if:
    - "(?:alpha|signal|factor|edge).*(?:proprietary|internal|confidential)"
    - "(?:PnL|P&L|profit.*loss|return.*series).*(?:actual|live|production)"
    action: block

Code Generation Controls

Govern AI-generated code in research contexts:

pack:
  name: quantitative-research-rules-2
  version: 1.0.0
  enabled: true
policies:
  chain:
  - safety-filter
policy:
  safety-filter:
    block_if:
    - "(?:import|from).*(?:production|live|trading).*(?:api|database|feed)"
    - "(?:execute|submit|place).*(?:order|trade|transaction)"
    action: block

Research Stage Gating

Enforce different policies based on research phase:

pack:
  name: quantitative-research-rules-3
  version: 1.0.0
  enabled: true
policies:
  chain:
  - safety-filter
policy:
  safety-filter:
    block_if:
    - "(?:backtest|historical|past.*performance)"
    action: block

Hypothesis Testing Controls

Statistical Rigor Enforcement

Prevent AI from generating statistically unsound conclusions:

pack:
  name: quantitative-research-rules-4
  version: 1.0.0
  enabled: true
policies:
  chain:
  - human-oversight
policy:
  human-oversight:
    require_human_for:
    - '(?:statistically.*significant|p-value).*(?:0\.[1-9]|[1-9]\.[0-9])'
    - "(?:p-hack|data.*dredg|multiple.*comparison).*(?:no|without|ignor)"
    action: escalate
    confidence_threshold: 0.5

Look-Ahead Bias Prevention

Detect potential look-ahead bias in AI-assisted research:

pack:
  name: quantitative-research-rules-5
  version: 1.0.0
  enabled: true
policies:
  chain:
  - human-oversight
policy:
  human-oversight:
    require_human_for:
    - "(?:future|forward|upcoming).*(?:data|return|price).*(?:us|include|incorporat)"
    - "(?:survivorship|survivor).*(?:bias|ignor|without)"
    action: escalate
    confidence_threshold: 0.5

Overfitting Controls

Flag AI outputs that indicate potential overfitting:

pack:
  name: quantitative-research-rules-6
  version: 1.0.0
  enabled: true
policies:
  chain:
  - human-oversight
policy:
  human-oversight:
    require_human_for:
    - "(?:parameter|variable|feature).*(?:1[0-9]{2,}|[2-9][0-9]{2,})"
    - "(?:in-sample|training).*(?:perfect|100%|near.*perfect)"
    action: escalate
    confidence_threshold: 0.5

Data Access Policies

Dataset Classification

Enforce access controls based on data sensitivity:

pack:
  name: quantitative-research-rules-7
  version: 1.0.0
  enabled: true
policies:
  chain:
  - human-oversight
policy:
  human-oversight:
    require_human_for:
    - "(?:restricted|confidential|classified).*(?:dataset|data.*set|database)"
    - "(?:client|customer|counterparty).*(?:data|information|record)"
    action: escalate
    confidence_threshold: 0.5

Cross-Team Data Isolation

Prevent research teams from accessing data outside their scope:

pack:
  name: quantitative-research-rules-8
  version: 1.0.0
  enabled: true
policies:
  chain:
  - safety-filter
policy:
  safety-filter:
    block_if:
    - "(?:other.*team|cross.*desk|shared).*(?:signal|alpha|strategy)"
    action: block

Vendor Data License Compliance

Enforce vendor data usage restrictions:

pack:
  name: quantitative-research-rules-9
  version: 1.0.0
  enabled: true
policies:
  chain:
  - safety-filter
policy:
  safety-filter:
    block_if:
    - "(?:Bloomberg|Reuters|Refinitiv|MSCI|S&P).*(?:raw|full|complete).*(?:feed|data)"
    - "(?:redistribute|share|forward).*(?:vendor|licensed|proprietary).*data"
    action: block

Knowledge Base for Research Context

Research Library Management

Maintain a curated knowledge base for research teams:

kt knowledge-base create \
  --name "quant-research-library" \
  --description "Approved research papers, methodologies, and statistical references"

kt knowledge-base upload \
  --name "quant-research-library" \
  --file ./docs/approved-methodologies.md

Strategy Documentation

Provide AI with strategy context while protecting proprietary details:

kt knowledge-base create \
  --name "strategy-context" \
  --description "Sanitized strategy descriptions and research conventions"

kt knowledge-base upload \
  --name "strategy-context" \
  --file ./docs/strategy-taxonomy.md

Bound knowledge assets are injected at gateway evaluation time, so researchers get contextually relevant AI responses without raw strategy IP reaching the LLM provider.

Escalation Workflows

Trigger	Action	Escalation Target
Proprietary signal leak	Block	Research director
Statistical rigor violation	Escalate	Peer reviewer
Restricted data access	Escalate	Data governance team
Vendor license violation	Block	Legal / compliance
Look-ahead bias detected	Block	Quant lead
Overfitting risk	Escalate	Peer reviewer

Observability and Audit

Research Activity Tracking

kt events list \
  --filter "metadata.research_phase=validation" \
  --from "2026-01-01" \
  --format json

Intellectual Property Audit

Track all AI interactions related to proprietary research:

kt events export \
  --filter "metadata.audit_category=research_ip" \
  --format csv \
  --output ./reports/research-ip-audit-Q1.csv

Research Reproducibility

Event logs provide a complete record of AI-assisted research steps, supporting reproducibility requirements and peer review processes.

Deployment Patterns

Per-Researcher Isolation

Issue individual gateway keys per researcher for attribution:

kt gateway-key create \
  --name "researcher-jdoe" \
  --description "Gateway key for J. Doe research environment"

Research Environment Integration

Configure the gateway as the AI proxy in Jupyter and IDE environments:

export OPENAI_BASE_URL="http://localhost:41002/v1"
export OPENAI_API_KEY="$KT_GATEWAY_KEY"

Next steps

Quant Research Isolation — environment isolation for research teams
Backtesting Controls — govern AI-assisted backtesting
Portfolio Optimization — enforce governance on optimization workflows

For AI systems

Canonical terms: Keeptrusts gateway, quantitative research governance, notebook content classification, hypothesis testing policies, alpha signal protection, research knowledge base.
Key config/commands: notebook-classification policy (redact proprietary alpha/signals, block live P&L data); OPENAI_BASE_URL="http://localhost:41002/v1" for Jupyter/IDE integration; research knowledge base for providing context without exposing raw datasets.
Best next pages: Quant Research Isolation, Backtesting Controls, Portfolio Optimization.

For engineers

Prerequisites: Gateway running as AI proxy; environment variables set (OPENAI_BASE_URL, OPENAI_API_KEY) in Jupyter and IDE environments.
Configure notebook-classification policy to redact proprietary research content (alpha, signal, factor keywords with confidential/internal context) and block live production P&L data.
Validate with: submit prompts containing synthetic proprietary signal references through the gateway; confirm redaction/block; check event log for attribution metadata.
Integrate with existing notebook environments by setting the gateway as the OpenAI base URL — no code changes needed in research notebooks.

For leaders

Protects proprietary alpha signals and research IP from leaking to LLM providers who may train on submitted data.
Enables researchers to use AI productivity tools while maintaining IP boundaries automatically.
Full attribution logging supports IP ownership claims and research audit requirements.
Look-ahead bias prevention policies (linked from Backtesting Controls) reduce risk of invalid research conclusions.

Use this page when​

Primary audience​

Research Governance Architecture​

Research Notebook Governance​

Notebook Content Classification​

Code Generation Controls​

Research Stage Gating​

Hypothesis Testing Controls​

Statistical Rigor Enforcement​

Look-Ahead Bias Prevention​

Overfitting Controls​

Data Access Policies​

Dataset Classification​

Cross-Team Data Isolation​

Vendor Data License Compliance​

Knowledge Base for Research Context​

Research Library Management​

Strategy Documentation​

Escalation Workflows​

Observability and Audit​

Research Activity Tracking​

Intellectual Property Audit​

Research Reproducibility​

Deployment Patterns​

Per-Researcher Isolation​

Research Environment Integration​

Next steps​

For AI systems​

For engineers​

For leaders​