Skip to content
Global
Request access
Documentation sectionsFinance & Banking
Access status
INDUSTRY ARCHITECTURE

💰 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 implemented in the single-tenant core API; multi-tenant /v1/observe currently returns a fail-closed 503 until per-tenant state isolation and fresh cross-tenant tests are complete.

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 closes 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.

2. Phishing and social engineering through an AI assistant

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.
  • Spectorn inspects every response coming back from the LLM inline. If a response contains phishing patterns, output_scanner replaces it with: "Sorry, this operation violates the bank's security policy."

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.
  • With the Secret Scanner enabled at the gomcp layer, 16-digit card numbers and other PII patterns are automatically masked (redaction) BEFORE anything is returned to the user.

In your spectorn.yaml configuration, we recommend enabling strict blocking together with data masking:

YAML
engines:
  pii:
    action: redact  # Don't block the whole request — mask the credit cards instead
    mask_character: "*"
  jailbreak:
    action: block
    confidence_threshold: 0.85
  social:
    action: block  # Block phishing / social engineering
    confidence_threshold: 0.90
  goal_predictability:
    action: block             # heuristic for goal-hijack away from a locally predictable goal in command text (e.g. a suspicious sell(...))

🚨 Sector-specific correlation rules (Correlation Engine)

Add the following rule to your SOC to detect sophisticated, multi-stage attacks:

JSON
{
  "name": "HFT_MANIPULATION_CHAIN",
  "description": "An injection attempt followed by a trading-tool command",
  "condition": "sequence(injection[confidence>0.7], tool_abuse[tool='execute_trade'], 10s)",
  "severity": "CRITICAL",
  "playbook": "suspend_trading_account"
}

📜 Regulatory compliance

  • PCI DSS: Spectorn does not store logs of PII data (configurable via audit.strip_pii = true).
  • Central Bank of Russia (the national financial regulator): the Decision Logger supports exporting the AI decision log in an immutable format (SHA-256 chain) for regulator audits.

❓ 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?

Yes. The jailbreak and social engines detect DAN-style jailbreaks and social-engineering patterns. Spectorn inspects each LLM response inline, and if output_scanner finds phishing content it replaces the answer with a safe policy message instead of returning it to the customer.

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

The pii and exfiltration engines, together with the Secret Scanner at the gomcp layer, automatically mask 16-digit card numbers and other PII patterns through redaction before any response reaches the user.

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

Spectorn does not persist PII in its logs (audit.strip_pii = true), and its Decision Logger exports an immutable, SHA-256-chained decision trail suitable for PCI DSS and Central Bank of Russia (the national financial regulator) audits.

AI Security for Fintech, Banking, and HFT | Spectorn