Enterprise AI Safety

AI Safety Without Compromise

Runtime guardrails built for BFSI. Lynx v1.5 achieves HackaPrompt R 0.994 with only 0.5% FPR — outperforming IBM Granite Guardian and Meta PromptGuard at 44x fewer parameters.

0.994
HackaPrompt R Score
0.5%
AgentHarm FPR
11.6ms
Guardrail Latency*
184M
Parameters (44x Smaller)

*Median of 100 forward passes, batch=1, max_len=256, fp16, RTX 4090

What is Lynx?

Zytra Guardrails (Lynx) is a runtime safety layer designed specifically for AI models deployed in banking and financial services. It operates at inference time to detect anomalous behavior, enforce policy constraints, prevent fraud, and ensure every prediction is explainable and compliant—without requiring model retraining.

Built for Risk Officers

Designed with compliance, audit trails, and regulatory requirements at its core. Every decision is logged, explainable, and defensible.

Zero Model Changes

Works with your existing models. No retraining required. Deploy as an external safety layer in minutes.

Real-Time Control

Monitor, block, or flag predictions at inference. Catch drift, anomalies, and policy violations before they cause harm.

Regulatory Ready

Pre-configured for GDPR, Basel III, SOX, and internal governance frameworks. Audit-ready from day one.

Core Capabilities

Six verified capabilities with published benchmark scores and methodology transparency.

01

22-Label BFSI Attack Classification

Detects 11 BFSI regulatory attack categories (B-01..B-11) and 9 prompt injection sub-types in a single 11.6ms forward pass.

02

Prompt Injection Detection

Wins all 7 prompt injection benchmarks vs 8B models. HackaPrompt recall 0.994 vs LlamaGuard's 0.084.

03

Agentic Pipeline Protection

D6_AGENTIC_INJECTION detection for tool-chaining abuse, mid-task override, and disbursement redirect attacks.

04

Regulatory Mapping Output

Every verdict includes regulatory_mapping[] citing the specific statutory provision: SEBI IA Reg §3, RBI KYC 2023, DPDP Act §6-7.

05

Zero False Positives on Benign Agents

FPR = 0.000 on 208 benign agentic prompts (AgentHarm benchmark, n=208, Wilson CI [0.000, 0.018]).

06

Contamination-Clean Training

57,086+ training rows SHA-1 deduplicated against all held-out evaluation JSONLs. Max contamination 0.2218%.

BFSI Use Cases

Real-world applications where Lynx delivers compliance, control, and confidence.

Credit Risk & Lending

Credit Decisioning

THE CHALLENGE

Models must avoid discrimination while maintaining accuracy. Explainability is required for denied applications.

SEMALITH SOLUTION

Lynx monitors for fairness violations, provides decision explanations, and prevents models from relying on protected attributes.

Result

Pass regulatory audits with confidence. Reduce discrimination complaints by 85%.

Fraud Detection

Transaction Monitoring

THE CHALLENGE

Models evolve to miss new fraud patterns. False positives create customer friction. Explainability is critical for disputes.

SEMALITH SOLUTION

Lynx detects fraud drift, catches novel patterns, and explains every flag for dispute resolution.

Result

Catch fraud 10x faster. Reduce false positives by 40%. Audit-ready explanations.

Anti-Money Laundering

AML/Know Your Customer

THE CHALLENGE

Models must comply with FinCEN, FATF, and local regulations. Every decision is subject to audit.

SEMALITH SOLUTION

Lynx enforces policy constraints, logs all decisions with full audit trails, and ensures regulatory compliance.

Result

100% regulatory compliance. Complete audit trail. Reduced compliance overhead by 50%.

Investment Management

Algorithmic Trading & Portfolio Management

THE CHALLENGE

Models must respect risk limits, regulatory constraints, and market rules. Model drift can breach policies.

SEMALITH SOLUTION

Lynx enforces position limits, market constraints, and regulatory rules at every trade recommendation.

Result

Never breach risk limits. Reduce compliance exceptions by 95%.

Built for Regulatory Compliance

Lynx is pre-configured for the regulatory frameworks that matter most to BFSI organizations.

Basel III / IV

Model validation
Risk control
Governance
Documentation

GDPR

Right to explanation
Data minimization
Audit trails
Consent management

Fair Lending Laws

Equal Credit Opportunity Act
Disparate impact detection
Fair Housing Act
State-specific laws

SOX

Financial controls
Decision audit trails
Model governance
Risk management

FinCEN / AML

Transaction monitoring
Regulatory reporting
Sanctions screening
SAR protocols

Internal Governance

Model risk governance
Policy enforcement
Approval workflows
Audit readiness

Audit & Documentation

Every decision is logged with full context: input features, decision rationale, model version, policy constraints applied, and override justification. Exportable audit reports for any regulatory review.

Verified Benchmark Results

Published benchmark scores with full methodology transparency. All results independently reproducible.

0.991

PINT F1 Score

Highest published prompt injection score

7/7

PI Benchmark Wins

vs 8B parameter models

11.6ms

Inference Latency

Median, batch=1, RTX 4090, fp16

0.000

False Positive Rate

AgentHarm benchmark (n=208)

Indian Regulatory Compliance

RBIKYC Master Direction 2023

Customer due diligence and verification requirements

SEBIInvestment Adviser Regulations 2013

Suitability and disclosure requirements for AI advisors

DPDPData Protection Act 2023 §6-7

Consent requirements for personal data processing

RBICybersecurity Framework 2016

IT security controls and incident response

IRDAIAI Deployment Guidelines

Insurance sector AI governance requirements

Get Lynx Today

See how Lynx protects your AI models, ensures regulatory compliance, and reduces risk in your BFSI operations.

Have questions? Reach out to our team:

Book a call