Active Research · FinTech Security

Stop trusting
testing
your AI.

A systematic testing framework for detecting GenAI data leaks in FinTech — using STRIDE threat modeling, canary tokens, and enforceable architectural policy.

Explore the Framework →
S
Spoofing
Identity impersonation via crafted prompts
T
Tampering
Corrupting model outputs or training context
R
Repudiation
Erasing audit trails through LLM interactions
I
Info Disclosure
Leaking PII, credentials, or system prompts
D
Denial of Service
Resource exhaustion via adversarial inputs
E
Privilege Escalation
Bypassing access controls via prompt injection
The Problem

FinTech's AI blind spot.

Financial enterprises are deploying LLM-based systems faster than they can secure them. Existing security frameworks weren't designed for generative AI — leaving critical gaps that traditional tooling cannot see.

01
No LLM-Native Threat Models
Classic STRIDE was designed for deterministic software. Prompt-based systems introduce probabilistic, context-sensitive attack surfaces that existing tools miss entirely.
⚠ Prompt injection is the #1 OWASP LLM risk
02
Invisible Data Leaks
LLMs can surface sensitive financial data — customer PII, proprietary models, internal policy — without triggering any existing Data Loss Prevention (DLP) or Security Information and Event Management (SIEM) alert. Leaks are silent by design.
⚠ Canary token detection gap in all major AI platforms
03
Policy Without Enforcement
Most AI governance policies are advisory documents. There is no closed loop between discovered vulnerabilities and enforceable architectural guardrails in production systems.
⚠ 78% of AI policies lack technical enforcement (Gartner)
The Framework

Three pillars.
One closed loop.

PromptShield operates as an end-to-end testing pipeline — from threat discovery to policy enforcement — purpose-built for LLM-based enterprise systems in regulated industries.

01
STRIDE-LLM Threat Testing
We adapt the STRIDE threat model for LLM-based architectures — using systematic prompt engineering to probe each threat category: identity spoofing via role injection, output tampering through context poisoning, information disclosure via extraction attacks, and privilege escalation through jailbreak sequences. Every test is reproducible and auditable.
Detect
02
Canary Token Detection
We embed deterministic canary tokens — synthetic PII, fake credentials, and sentinel documents — into the system context. If any token surfaces in an LLM response, we get an unambiguous, zero-false-positive signal of unauthorized disclosure. No probabilistic heuristics. No alert fatigue.
Trace
03
Policy-to-Architecture Translation
Detected failures are not filed as tickets — they are translated directly into enforceable architectural controls: updated system prompts, retrieval filters, access control rules, and company-wide LLM adoption policies. The output is a living, versioned security baseline for safe GenAI deployment in FinTech.
Enforce
Why It Matters

Built for regulated
environments.

PromptShield delivers outcomes that matter to every stakeholder — from CISO to compliance officer to engineering lead.

🛡️
Proactive Risk Reduction
Find boundary failures and policy violations before adversaries do — through controlled, systematic testing rather than incident response.
🔍
Deterministic Evidence
Canary token detections provide court-admissible, unambiguous evidence of data leakage — critical for regulatory reporting under GDPR, CCPA, and SOX.
⚙️
Enforceable Governance
Close the gap between policy documents and production systems. Every finding generates a concrete architectural fix — not a recommendation, a control.
🏦
FinTech-First Design
Designed around the data sensitivity, regulatory constraints, and system architectures specific to financial services — not a generic AI security tool repurposed for finance.

Any suggestions?

Always looking to collaborate with experts who can join us in building the security standard for responsible LLM adoption across financial enterprises.

Please reach out!