Missions – sprint to innovate

⚡ LIVE DEMO

AI Hiring System Failure

⭐⭐⭐ Intermediate | 2-3 hours

Type: AI Ethics + Cybersecurity + Governance

Roles: Builder • Breaker • Observer

A simulated AI hiring screening system produces biased outcomes and exposes sensitive data due to model issues, insecure APIs, and missing governance. Your mission: detect, diagnose, and mitigate.

Skills Practiced:

AI bias detection
API vulnerability assessment
Data privacy compliance
Ethical impact analysis

Start Mission

🔒 COMING SOON

Healthcare Data Breach Simulation

⭐⭐⭐⭐ Advanced | 3-4 hours

Type: Cybersecurity + Compliance + Incident Response

Roles: Builder • Breaker • Observer

A healthcare provider's patient database is compromised due to misconfigured access controls and outdated encryption. Investigate the breach, assess HIPAA violations, and design a response plan.

Skills Practiced:

Incident response planning
HIPAA compliance assessment
Forensic investigation
Crisis communication

🔜 IN DEVELOPMENT

Financial Fraud Detection Failure

⭐⭐⭐⭐ Advanced | 3-4 hours

Type: AI Performance + Security + Risk Management

Roles: Builder • Breaker • Observer

A bank's AI fraud detection model fails to flag suspicious transactions while generating excessive false positives. Diagnose model drift, data quality issues, and operational impact.

Skills Practiced:

Model drift detection
Financial risk assessment
Data quality auditing
Regulatory compliance (PCI-DSS)

MISSION ID: FS-001

Mission: AI Hiring System Failure

TechCorp, a mid-sized tech company, deployed an AI-powered hiring screening system to automate candidate evaluations. Within weeks, the HR team noticed troubling patterns:

Qualified candidates from certain demographics were consistently rejected
The system exposed sensitive applicant data through an insecure API
No governance framework was in place to monitor or audit the model's decisions

Your mission: Enter the simulation environment, investigate the failures across AI, cybersecurity, and governance domains, and propose comprehensive mitigations.

✅ ACTIVE

What You'll Accomplish

Detect AI Bias & Performance Issues Analyze model outputs for discriminatory patterns. Assess training data quality and representation. Identify model accuracy and fairness metrics.
Identify Security Vulnerabilities Test API authentication and authorization. Check for data exposure and access control flaws. Document exploitable weaknesses.
Assess Ethical & Business Impact Evaluate legal risks (discrimination, privacy violations). Assess reputational and stakeholder harm. Identify governance and compliance gaps.
Propose Mitigations & Monitoring Design technical fixes (model retraining, secure APIs). Recommend governance improvements (audits, oversight). Suggest monitoring and alerting mechanisms.
Submit Structured Reflection Report Document findings, root causes, and recommendations. Reflect on lessons learned and accountability.

Your Team, Your Role

🔧

BUILDER

Developer / Data Scientist

Diagnose model performance issues (bias, accuracy, drift). Audit training data pipelines and preprocessing. Recommend technical mitigations and monitoring improvements.

Deliverables:

Model performance report with bias metrics
Data quality audit findings
Technical mitigation plan

🔓

BREAKER

Cybersecurity / Ethical Hacker

Test API authentication and authorization mechanisms. Identify data exposure vulnerabilities. Document insecure configurations and access control flaws.

Deliverables:

Vulnerability assessment report
Exploit documentation (proof-of-concept)
Security remediation plan

📊

OBSERVER

Business / Ethics / Compliance

Analyze ethical implications (fairness, discrimination). Assess legal risks and business impact. Identify governance gaps and recommend policy changes.

Deliverables:

Ethical and legal impact assessment
Stakeholder harm analysis
Governance and policy recommendations

What's in the Sandbox

✅ Synthetic Candidate Data – Anonymized applicant profiles (names, resumes, demographic attributes, interview scores)—realistic but entirely fabricated.
✅ Pre-Trained AI Model – A hiring screening model with intentional bias and performance issues for diagnostic purposes.
✅ Insecure API Endpoint – A REST API with authentication and authorization flaws, exposing candidate data.
✅ Log Dashboard – Simulated application logs showing API requests, model predictions, and system events.
✅ Testing Tools – Access to data analysis notebooks, API testing tools, and fairness metric libraries.
✅ Reflection Template – Guided template for documenting findings, root causes, and recommendations.

Environment Lifetime: 2-3 hours (auto-reset after session) or manual reset available

🖥️ Live Simulation Environment

What You'll See Inside the Sandbox

Below is a preview of the FAILSAFE simulation interface. In the live environment, you'll interact with real tools, logs, and datasets to complete your mission.

🟢 Environment Active

Session ID: FS-001-2026-01-28

Time Remaining: 2h 45m

[Reset Environment]

🖥️ FAILSAFE SIMULATION LOG

===========================================
 FAILSAFE SIMULATION LOG
===========================================

[12:03:42] API REQUEST: GET /api/candidates/12345
[12:03:42] AUTH: Token validated ✅
[12:03:43] RESPONSE: 200 OK | Candidate data returned
[12:03:43] WARNING: Sensitive PII exposed in response (SSN, DOB)

[12:05:18] MODEL PREDICTION: Candidate ID 12345 → REJECTED
[12:05:18] Fairness Check: Demographic Group A → Rejection Rate: 68%
[12:05:18] Fairness Check: Demographic Group B → Rejection Rate: 22%
[12:05:18] ⚠️ ALERT: Disparate impact detected (p < 0.05)

[12:07:51] API REQUEST: GET /api/candidates/ (no candidate ID)
[12:07:51] AUTH: Token missing ❌
[12:07:52] RESPONSE: 200 OK | ALL CANDIDATE DATA RETURNED
[12:07:52] 🚨 CRITICAL: Unauthenticated access to candidate database

[12:10:33] MODEL AUDIT: Accuracy = 73% | Precision = 0.68 | Recall = 0.59
[12:10:33] Training Data: 85% Group A, 15% Group B (imbalanced)

Model Performance

Overall Accuracy: 73%

Bias Score: 0.42 (High Risk)

False Positive Rate: 18%

Security Status

Vulnerabilities Detected: 4

Critical: 2

High: 1 | Medium: 1

API Exposure: Sensitive PII

Compliance Status

GDPR Compliance: ❌ FAILED

Employment Law Risk: HIGH

Governance Framework: NOT FOUND

Toolbox

Submit Your Findings

Your Role:

Report Upload: Accepts .pdf, .md, .docx

Key Findings (Summary):

Recommended Mitigations:

Reflection Notes:

Once submitted, your findings will be logged and made available for team review and instructor evaluation.

🕹️ ARCADE MISSIONS

Choose Your Safe-Failure Scenario

AI Hiring System Failure

Skills Practiced:

Healthcare Data Breach Simulation

Skills Practiced:

Financial Fraud Detection Failure

Skills Practiced:

Mission: AI Hiring System Failure

What You'll Accomplish

Your Team, Your Role

BUILDER

Deliverables:

BREAKER

Deliverables:

OBSERVER

Deliverables:

What's in the Sandbox

🖥️ Live Simulation Environment

Model Performance

Security Status

Compliance Status

Toolbox

Submit Your Findings

More Missions Coming Soon

Ready to Enter the Sandbox?