💰 Spectorn AI Security for Fintech, Banking, and HFT Trading

Target audience: Banks, brokers, HFT funds, fintech startups.

Current status: This page describes the threat model, implemented core primitives, and a target integration profile. Spectorn sees only events that a calling system actually submits, and blocks only where that system enforces its verdict. Structured temporal_safety is served through hosted /v1/observe and keeps no state between calls.

In the financial sector, deploying Large Language Models carries the risk of catastrophic monetary loss. Whether it is an AI assistant inside a banking app or an autonomous HFT agent on an exchange, Spectorn provides detection and control primitives for integration into a defensive boundary.

🛑 Key risks and how Spectorn addresses them

1. Manipulation of HFT trading agents (Prompt Injection)

Risk: An attacker plants hidden prompt injections inside news articles or social media posts, coercing an HFT agent into selling assets at an artificially low price. Spectorn defense:

Engines: injection, goal_predictability.
The goal_predictability engine heuristically flags when the agent's final command (for example, sell(msft, 1000)) looks like it pushes the system away from a locally predictable goal. Structured temporal_safety can check a trade sequence through a tenant-owned core registry; the hosted multi-tenant intake remains closed.

Risk: An attacker uses a "jailbreak" technique (such as DAN mode) to make a bank's official chatbot advise a customer to transfer money to a fraudulent account. Spectorn defense:

Engines: jailbreak, social.
In gateway mode Spectorn also checks the model's response inline; when the policy blocks it, the client receives a 403 spectorn_output_blocked instead of the response, and your application decides what the user sees.

3. Leakage of card data and balances (Data Exfiltration)

Risk: The model memorizes PII (card numbers, balances) from conversation history or the RAG corpus and accidentally discloses it to another user. Spectorn defense:

Engines: pii, exfiltration.
pii (with its alias secret_scanner) helps find 16-digit card numbers and other PII patterns in the response; in gateway mode with enforcement such a response does not reach the user. Check the resulting policy on real traffic and the bank's own test corpus.

🛠️ Policy settings

There is no configuration file to deploy: the platform operator sets your tenant's policy at your request, including the scan mode (enforce, or shadow to see what would be blocked without blocking it), allow and deny lists, the block threshold and category weights. The engines that matter most for this sector: pii, jailbreak, social, goal_predictability. Start in shadow mode on your own traffic, review what would have been blocked, then enforce. Masking of personal data is not enabled in the cloud contours: a request with personal data is blocked or flagged, not rewritten.

🚨 Correlation in your SIEM

Spectorn sends each finding to the SIEM you configure as a JSON event over HTTPS (spectorn.ai-finding.v1), with the rule, category, severity and verdict_id. Build multi-stage rules for this sector there, for example: an injection attempt followed by a trading-tool command. Spectorn's own SOC correlation (GoMCP) is not deployed in the RU and Global cloud contours.

📜 Regulatory compliance

Spectorn does not make a deployment compliant by itself. It gives evidence your programme can use for PCI DSS, Central Bank of Russia (the national financial regulator):

the scan log keeps a hash of the prompt, not its text, and content is not stored by default;
decisions are recorded in a tamper-evident SHA-256 chain;
findings can be delivered to your SIEM.

Interpreting this evidence against a specific framework remains the work of your auditor and your organisation.

❓ FAQ

How do I prevent prompt injection from manipulating an HFT trading agent?

An integration can submit news, social feeds, and messages to injection, and commands to goal_predictability; its external orchestrator must enforce the resulting verdict before execution. Structured temporal_safety remains a core capability, but its hosted multi-tenant HTTP intake is currently unavailable.

Can Spectorn stop a banking chatbot from being jailbroken into giving fraudulent advice?

It helps. The jailbreak and social engines detect DAN-style jailbreaks and social-engineering patterns. In gateway mode the model's response is scanned as well; when the policy blocks it, the client receives a 403 spectorn_output_blocked instead of the response, and your application decides what the user sees.

How does Spectorn keep card numbers and account balances from leaking to the wrong user?

The pii and exfiltration engines find 16-digit card numbers and other PII patterns in the response; in gateway mode with enforcement a blocked response does not reach the user (HTTP 403 spectorn_output_blocked). Masking is not enabled in the cloud contours, so the response is withheld rather than redacted.

Is Spectorn compatible with PCI DSS and Central Bank of Russia audit requirements?

Spectorn's scan log keeps a hash of the prompt, not its text, and content is not stored by default.