Our Methodology

Evidence-based risk assessment powered by 100,000+ documented incidents through AI and Complex Event Processing technology

Evidence-Based Risk Assessment

Our proprietary AI-powered risk intelligence platform analyzes 100,000+ documented incidents from multiple global data sources through AI and Complex Event Processing (CEP) technology. Every risk score is calculated from real-world failures, not theoretical models.

Our intelligent engine assesses risks from remote data sources across 200+ sources and 40+ jurisdictions, providing transparent, defensible, and actionable insights.

Our Key Principles

Evidence-Based

Every risk score is calculated from real incidents, not theoretical models or frameworks.

100,000+ documented incidents
200+ remote data sources
40+ jurisdictions covered
AI-powered analysis with CEP technology

Contextual Matching

We match your specific context (industry, use case, tech stack) to the most relevant incidents.

Industry-specific analysis
Use case similarity scoring
Technology stack matching
Company size considerations

Transparent & Defensible

See exactly which incidents contributed to your score. No black boxes, no mystery algorithms.

Full incident attribution
Relevance scoring (0-100)
Match factor explanation
Audit-ready reports

How We Calculate Your Risk Score

Context Collection

We gather detailed information about your AI system:

Industry sector and regulatory environment
Use case (e.g., content generation, decision automation)
Technology stack (models, frameworks, deployment)
User base size and sensitivity of data
Current controls and safeguards in place

Incident Matching

We search our database of 100,000+ incidents from 200+ remote data sources to find the most relevant failures:

Industry match: Companies in your sector
Use case similarity: Similar AI applications
Technology alignment: Comparable tech stacks
Scale matching: Similar user base size
Control gaps: Systems with similar vulnerabilities

Relevance Scoring

Each incident is scored for relevance to your context (0-100%):

90-100%: Direct matches (same industry, use case, tech)
70-89%: High similarity (2 of 3 factors match)
50-69%: Moderate similarity (1 of 3 factors match)
30-49%: General relevance (analogous scenarios)
Below 30%: Excluded from calculations

Risk Calculation

We aggregate the matched incidents into quantitative risk scores:

Likelihood Score (0-100): Frequency of similar failures
Impact Score (0-100): Average severity of consequences
Financial Risk: Estimated potential losses
Regulatory Risk: Compliance violation probability
Overall Risk Score: Weighted combination of all factors

Attribution & Transparency

We show you exactly which incidents influenced your score:

Top 10 most relevant incidents displayed
Incident details: Company, date, failure type, impact
Match factors: Why this incident is relevant to you
Weight contribution: How much it influenced the score
Source links: Original incident reports and analyses

Remote Data Sources & Technology

Our intelligent engine aggregates data from 200+ remote data sources across multiple domains, including research papers, security databases, regulatory filings, and industry reports. We use AI and Complex Event Processing (CEP) technology to analyze and correlate incidents in real-time.

Data Coverage

• 100,000+ documented incidents
• 200+ remote data sources
• 40+ jurisdictions monitored
• 20K+ regulatory violations tracked

Technology Stack

• AI-powered risk analysis
• Complex Event Processing (CEP) for real-time correlation
• Semantic search and vector embeddings
• Continuous updates from remote sources

Quality & Accuracy

Quality Metrics

✓ 89.3% prediction accuracy
✓ 95%+ source reliability
✓ 200+ active remote sources
✓ Continuous quality control

Update Frequency

✓ Daily updates for critical sources
✓ Real-time CEP processing
✓ Continuous data ingestion
✓ Historical archive maintained

Limitations & Caveats

While our methodology is evidence-based and data-driven, it's important to understand its limitations:

Historical Data Bias: Our scores are based on past incidents. Emerging risks without historical precedent may be underestimated.
Reporting Bias: Not all incidents are publicly disclosed. High-profile companies may be overrepresented in our database.
Context Limits: We match based on available information. Unique or highly specialized systems may have fewer comparable incidents.
No Guarantees: Our scores are estimates based on historical patterns. They should inform, not replace, expert judgment and due diligence.

Use our risk scores as one input in your decision-making process, alongside security audits, penetration testing, and expert consultation.

See Our Methodology in Action

Get a free risk assessment and see exactly which incidents inform your score

Try Free Assessment