BOLA / Account Takeover Cross-Service Auth Drift Cashback & Reward Exploitation Replay & Idempotency Failures AI Workflow Skip-Step Abuse GraphQL Field Exposure Shadow API Exposure Fraud-Driven API Chaining Webhook Manipulation Release-Level Auth Drift BOLA / Account Takeover Cross-Service Auth Drift Cashback & Reward Exploitation Replay & Idempotency Failures AI Workflow Skip-Step Abuse GraphQL Field Exposure Shadow API Exposure Fraud-Driven API Chaining Webhook Manipulation Release-Level Auth Drift

AI/LLM Platforms

Securing Every
AI System Workflow
Before It Ships

In modern AI platforms, every model interaction is an API workflow.
Apiezy validates data access boundaries, prompt execution workflows, and authorization integrity across distributed AI systems — before production.

0+
Attack Vectors
0%
Production Exposure
CI/CD
Integrated Testing
0ms
Shift-Left Enforcement
Request a Demo Explore All Use Cases
SCROLL TO EXPLORE
In Modern AI Platforms

Every Model
Interaction is an API

Model inference is an API
Prompt processing is an API
Embedding pipelines are APIs
Vector search is an API
Data ingestion is an API
Model integrations are APIs
Capability Gap

Where Traditional Tools Fall Short

Multi-step Workflow SequencingSAST and DAST cannot validate stateful, cross-service flows
Cross-Service Authorization IntegrityToken trust boundaries break silently between microservices
Economic Logic EnforcementBusiness-rule exploits use valid credentials — scanners are blind
Fraud-Driven API ChainingEach API call looks legitimate; combined, they're exploitative
Real-World Failure Patterns

09 Attack Vectors
Apiezy Eliminates

Fintech security risk lives in multi-step, stateful workflows — not just individual endpoints.

01
BOLA

Prompt Injection & Workflow Manipulation

Apiezy Prevention
  • Systematic cross-user identity substitution testing
  • Object ownership enforcement validation
  • Role-based access consistency across services
  • Authorization drift detection across endpoints
Ownership violations caught in CI/CD before exposure
02
Auth Drift

Model API Authorization Drift

Apiezy Prevention
  • Token scope (aud, iss, scope, role) across services
  • Trust-boundary transition simulation
  • Implicit trust detection between microservices
  • Distributed authorization integrity testing
Authorization drift identified before release
03
Economic Abuse

Cashback & Reward Exploitation

Apiezy Prevention
  • Multi-step workflow simulation and abuse modeling
  • State transitions across transactions validated
  • Reward eligibility enforcement testing
  • Circular transaction pattern detection
Economic logic abuse caught in pre-production
04
Replay Attack

Replay & Idempotency Failures

Apiezy Prevention
  • Replay attempts with modified headers tested
  • Duplicate transaction submission validation
  • Idempotency key reuse scenario coverage
  • Timestamp and nonce validation logic
Replay vulnerabilities found before financial loss
05
Workflow Bypass

AI Workflow Skip-Step Abuse

Apiezy Prevention
  • Valid workflow chain identification
  • Out-of-order API execution attempts
  • Precondition enforcement testing
  • Mandatory state transition validation
Workflow bypass fails during pre-production testing
06
GraphQL

Vector Database Data Leakage

Apiezy Prevention
  • Field-level access control testing
  • Cross-role query simulation
  • Unauthorized data exposure identification
  • Composite leakage via nested query detection
Sensitive financial fields protected before release
07
API Chaining

Multi-Agent Workflow Abuse

Apiezy Prevention
  • Composite workflow modeling
  • Circular transaction flow detection
  • Economic abuse vector testing
  • Cross-service state enforcement validation
Complex exploit chains identified in CI/CD
08
Partner Risk

Partner Integration & Webhook Manipulation

Apiezy Prevention
  • Webhook signature enforcement validation
  • Replay protection testing
  • Partner-domain trust validation
  • Cross-service state reconciliation
Partner-based abuse vectors mitigated pre-production
09
Release Risk

Model Release Authorization Drift

Apiezy Prevention
  • Release-over-release trust grading
  • Authorization behavior comparison between builds
  • Newly introduced exposure detection
  • Regression integrity validation
Trust degradation detected before deployment
Capability Gap Analysis

Why Traditional Tools Miss
What Apiezy Catches

SAST

Identifies static code patterns and syntax vulnerabilities at the source level.

Cannot prove runtime ownership or sequencing correctness

DAST

Probes endpoints for known injection and exposure patterns.

Requires manual scripting for identity-aware workflow simulation

Runtime API Security

Detects anomalies and monitors live API traffic patterns.

Detects only after exposure begins; depends on tuning signals

Observability / APM

Shows performance, traces, and service health across systems.

Shows performance and traces — not exploitability

Core Capabilities

What Makes Apiezy
Fundamentally Different

A workflow-intent enforcement engine — not a vulnerability scanner.

Stateful Workflow ModelingEnd-to-end multi-step financial workflow simulation across services
Cross-User Identity SubstitutionSystematic BOLA detection across all user and role combinations
Cross-Service Token Scope ValidationTrust boundary integrity across distributed microservice environments
Economic Abuse SimulationBusiness-rule exploit modeling including cashback and reward stacking
Replay & Idempotency EnforcementTransaction-state correctness validation under replay and concurrency
CI/CD Pre-Production EnforcementAutomated trust grading integrated directly into your release pipeline
GraphQL Resolver ValidationField-level and cross-role authorization testing for GraphQL APIs
Multi-Step Exploit Chain SimulationComposite fraud workflow detection across chained API calls
Release-Level Trust GradingRegression integrity analysis comparing authorization behavior across builds
Strategic Value

Built for Every
Financial Decision-Maker

For CTO
Reduce logic-level production escapes across microservices
Improve secure release velocity without slowing CI/CD
Lower regression risk across distributed financial workflows
For CISO
Strengthen controls aligned to MITRE ATT&CK techniques
Reduce business-logic attack surface systematically
Improve audit confidence with pre-production evidence
For CFO
Reduce direct fraud exposure from economic abuse vectors
Lower manual security validation cost through automation
Reduce emergency remediation overhead post-breach
Executive Summary

In AI-driven platforms, security is no longer just about blocking malicious input — it's about continuously validating that financial workflows, authorization boundaries, and business logic behave exactly as intended across distributed services.

Request a Demo All Use Cases