Your Trading Algorithms Cannot Be in an LLM Training Corpus
Financial services firms build competitive advantage on proprietary algorithms. When developers use AI coding tools to build those systems, the algorithms travel to third-party LLM providers. Pretense prevents that without slowing down engineering velocity.
The Problem
Why existing controls do not address AI coding tool risk for financial services teams.
Proprietary algorithms are primary competitive assets
A high-frequency trading strategy, a credit risk scoring model, or a fraud detection algorithm represents years of quantitative research. Unlike source code vulnerabilities, which can be patched, a leaked algorithm strategy cannot be un-leaked. The competitive damage is permanent.
Regulators are scrutinizing AI tool usage
The SEC, FINRA, FCA, and other financial regulators are issuing guidance on AI tool risk in financial services. Documented controls for AI data transmission are becoming a regulatory expectation, not just a best practice. Firms without these controls are accumulating regulatory risk.
AI tools are now embedded in financial engineering workflows
You cannot stop developers from using Copilot and Claude Code. They use them for boilerplate, documentation, test generation, and refactoring. In doing so, they inevitably expose context from proprietary systems. The question is not whether to allow AI tools, it's how to allow them safely.
How Pretense Solves It
Algorithm identifier mutation before LLM transmission
When a developer uses an AI tool on a trading algorithm, Pretense replaces every function name, class name, and variable name with a deterministic synthetic token. The LLM receives complete, coherent code. calculateMonteCarloVar becomes _fn4a2b. The algorithm structure is preserved. The strategy is not.
Regulatory-grade audit trail
Every AI tool request is logged with provider, timestamp, mutation count, and blocked content. Pretense generates audit reports in PDF and JSON formats aligned with financial services compliance requirements. Audit logs can be forwarded to your SIEM for centralized evidence management.
On-prem deployment for trading firm environments
Many financial services firms operate in co-location environments or private data centers with restricted outbound connectivity. Pretense deploys fully on-premises via Docker Compose or Kubernetes Helm charts. The proxy, dashboard, and audit store run in your infrastructure.
SOX, GLBA, and PCI-DSS alignment
Pretense audit controls map to SOX Section 404 (IT general controls), GLBA Safeguards Rule (customer financial data), and PCI-DSS Requirement 7 (restrict access to system components). The compliance report export includes control mapping documentation.
Compliance Coverage
Pretense generates audit evidence and compliance documentation for the frameworks that matter to financial services teams.
IT general controls evidence for Section 404
Safeguards Rule alignment for financial data
Requirement 7 access restriction documentation
Audit export for Type II controls
Kubernetes and Docker deployment
Splunk, Sentinel, Elastic connectors
What the LLM Actually Sees
Pretense transforms proprietary identifiers into synthetic tokens before transmission. Structure and logic are preserved. Your IP is not.
// Sent to LLM provider verbatim
async function fetchPatientMedicalHistory(
patientId: string,
includeSSN: boolean
) {
return await ehrClient.getRecord(
patientId, ENCRYPTION_KEY
);
}// Pretense-mutated before transmission
async function _fn4a2b(
_v8c3d: string,
_v2f1a: boolean
) {
return await _v9e4b._fn7d2c(
_v8c3d, _v6b1a
);
}After the LLM responds, Pretense reverses every mutation. You receive real, working code with your original identifiers restored byte-for-byte.
Frequently Asked Questions
How does Pretense handle high-frequency trading algorithm code?
Pretense mutates identifiers at the token level. Function names, variable names, and class names in HFT algorithms are replaced with synthetic tokens before any AI tool transmits them. The algorithm structure and timing relationships are preserved. Only the identifiers are obscured.
What about quantitative research notebooks?
Pretense protects AI API traffic from all tools that use OpenAI or Anthropic API endpoints. If developers use AI tools within Jupyter notebook workflows, Pretense intercepts that traffic the same way it handles IDE-based AI tools.
Can Pretense produce evidence for SEC or FINRA audits?
Pretense compliance reports document AI tool usage controls: what was protected, what was blocked, and how the technical controls operated over the audit period. The report format and available metadata can be shared with your compliance counsel to determine fit for specific regulatory requirements.
Does Pretense affect latency in trading-adjacent development environments?
Pretense adds under 10ms of latency to AI API calls. This applies to developer tooling, not to production trading infrastructure. AI coding tools are not in the trade execution path. Developer productivity tooling latency at this level is imperceptible.
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Protect your financial services team in 30 seconds
One environment variable. No code changes. No workflow disruption. Pretense intercepts every AI API request from day one.
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