Database Scan by ThunderScan

ThunderScan delivers a comprehensive database health check in a single secure scan — evaluating structure, data integrity, compliance posture, and AI readiness. Get a unified, actionable view so your engineering and data teams can move forward with confidence.

Read-only No data retained Results in minutes
What Gets Scanned
Schema & Architecture
Normalization, FK integrity, index gaps, RLS, God-tables
Data Integrity & Security
SOC2, GDPR, HIPAA — PII detection, encryption, tenant leaks
Data Quality & AI Readiness 8 dimensions
Completeness, accuracy, consistency — ML & vector schema ready
Scan My Database
Schema · Data Integrity · AI Readiness SOC2 · GDPR · HIPAA compliant scan
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HOW IT SCANS
The Scan Process

Five automated steps — from a secure read-only connection to prioritized, fix-ready output. No data ever leaves your environment. Every finding is deterministic, evidence-linked, and reproducible.

Workflow Process

How ThunderScan Works

ThunderScan works by securely connecting to your database in read-only mode and performing a deep, automated evaluation of your entire schema and underlying data. Once you establish a secure connection, ThunderScan extracts metadata — including tables, keys, indexes, constraints, and structural relationships — then applies both deterministic checks and AI-driven semantic analysis to identify design quality issues, referential integrity gaps, normalization problems, and missing indexes. Beyond the structural assessment, it also scans for sensitive data exposure, compliance risks (e.g., SOC 2/GDPR), and data quality issues such as completeness, consistency, and duplicate records, generating prioritized findings and detailed scorecards that quantify both architectural health and AI readiness.

Secure Connection

Steps 1–2: Establish & sample

AI Analysis

Step 3: Pattern detection

Actionable Output

Steps 4–5: Reports & fixes

1

Connect

Secure read-only access via JDBC/ODBC

2

Scan

Metadata extraction & sampling

3

Analyze

AI-driven pattern & anomaly detection

4

Report

Prioritized findings & scorecards

5

Remediate

Auto-generated fix scripts

Now you know how it scans.
Next: see exactly what it examines across three dimensions.
WHAT IT SCANS
The Scan Coverage

Three dimensions — structural architecture, security & compliance, and AI readiness — evaluated in a single automated pass. Unlike point tools, ThunderScan gives you a unified health picture you can act on immediately.

Core Capabilities

Three dimensions of comprehensive Database Scan

ThunderScan covers the three dimensions that determine whether a database is production-grade, compliant, and AI-ready. Each dimension maps to a distinct failure mode that teams typically discover too late — during an incident, an audit, or a failed ML training run. Unlike point tools that check one thing in isolation, ThunderScan evaluates all three in a single automated pass, giving engineering and data teams a unified health picture they can act on immediately.

Database Schema & Architecture

Complete structural analysis of your database design and relationships — identify God-tables, EAV creep, and normalization violations before they compound.

Referential Integrity & FK validation
Normalization (1NF–3NF) analysis
Index optimization & missing index detection
Multi-tenant Row-Level Security audit
Maturity scoring (0–5 scale)

Data Integrity & Security

SOC2, GDPR, and HIPAA readiness validation. Detect PII sprawl, weak hashing, tenant leaks, and encryption gaps before auditors do.

PII/PHI detection & classification matrix
Encryption & hashing verification (Argon2/bcrypt)
SOC2 Type II readiness controls
RBAC & access controls audit
Immutable audit trail validation

Data Quality & AI Readiness

8-dimension data quality framework mapped to AI/ML readiness. Your data must be clean, consistent, and complete before AI can add value.

Completeness ≥99% validation
Consistency & accuracy scoring
Duplicate detection & uniqueness checks
Feature engineering readiness assessment
Vector/embedding schema optimization
Framework

8 Data Quality Dimensions ThunderScan Measures

Production-grade target benchmarks
≥99%
Completeness
No critical NULLs in required fields
≥98%
Accuracy
Values conform to expected formats
100%
Consistency
No conflicting records across tables
≥99%
Validity
Data conforms to business rules
≤0.1%
Uniqueness
Duplicate records below threshold
≤5 min
Timeliness
Data freshness for operational use
0%
Integrity
Zero orphaned FK records allowed
100%
Traceability
Full audit trail on all changes
AI Agent

What is THOR?

THOR (Transformer Heuristics for On-Demand Retrieval), the AI Agent that leverages proprietary Text-to-SQL (T2S) to translate natural language into secure, optimized database actions. THOR allows you to talk to the data.

Deep Schema Understanding

Instantly maps natural language to complex table structures — no manual schema navigation.

Safe Read-Only Access

Analyzes data without risking integrity or production stability. Zero write access.

Instant Remediation

Generates precise SQL fixes automatically — cutting investigation time by 90%.

Insight Engine Workflow

From natural language to actionable business intelligence.

1
DB Schema
Structure Metadata & Constraints
2
Text to SQL
LLM Logic Converter
3
Execution
Read-Only Query on SaaS DB
4
Insights
Structured Data & Business Context
See THOR in Action
Ask questions in plain English and watch THOR turn them into instant data insights — no SQL required.
Try THOR Demo
Security

Your Data is Protected

Multi-layer security architecture ensures your data never leaves your control — trust Thor ThunderScan with complete confidence.

Secure database connections diagram
You Initiate Scan
TLS 1.3 / SSH IP Whitelisted
ThunderScan Metadata Only
Read-Only Access
Your DB No Write Access
Ephemeral Results
Report Zero Storage

Read-Only & Safe

Thor connects strictly in read-only mode. Permissions verified before every scan. Zero risk of data modification.

End-to-End Encryption

All data in transit protected via SSH tunnels and SSL/TLS. Credentials encrypted using Hardware Security Modules (HSM).

Zero Data Retention

Metadata analyzed in memory and discarded after reporting. Your customer records are never stored on our servers.

Enterprise-grade Security
SOC2 Compliant Infrastructure
Your Database Secrets Stay Yours
TLS 1.3 Encrypted
ROI & Business Impact

ROI for Database Excellence

ThunderScan's ROI is simple: the cost avoidance from one prevented incident typically exceeds the entire annual investment. Industry data: 39% of teams still use manual testing — every one is a deployment risk. 61% have undergone compliance audits (81% in finance). Here's the math.

Cost Avoidance

Tenant leak incident $500K+
Average data breach cost $4.5M
50% of hybrid orgs hit a security incident 1 in 2
Scale refactor cost $500K–$3M

Revenue Enablement

AI features ship faster (AI-ready schema) 30% faster
Compliance deal unlocks $500K+
Avoid abandoned AI projects (60% at risk) Save 2–3 yrs
ARPU uplift from production AI features 20% uplift

Infrastructure Savings

Compute reduction via partitioning 30–50%
Storage via archival & cleanup 20–40%
Query time via index fixes 60–90%
Engineering hours saved/mo 40–80 hrs

The Hidden Cost: Team Coordination Failures & Skill Debt from Poor Schema

49%
Report job-description mismatch
Rapid AI shifts mean teams don't know who owns schema health
48%
Skill-set & training barriers
Teams lack expertise to audit and fix schema problems proactively
76%
Organizations offer AI guidance
Yet 60% of AI projects still fail on data quality — guidance alone isn't enough
40–80
Engineering hours saved / month
When ThunderScan replaces manual schema audits and testing scripts

ROI Formula

Component 1
Cost Avoidance
(incidents prevented)
+
Component 2
Revenue Uplift
(AI features, deals)
+
Component 3
Infra Savings
(compute, storage)
−
Investment
ThunderScan
(total cost)
Deployment Strategy

Implementation Roadmap

From initial connection to continuous monitoring in 6 weeks.

Timeline estimates based on typical enterprise environments.

Setup & Discovery

Week 1–2
Establish secure connection & map schema metadata
Generate initial health scorecard & priority findings

Remediation Plan

Week 2–3
Review critical findings & approve fix scripts
Establish AI-readiness criteria benchmarks

Implementation

Week 3–6
Execute validated schema optimizations & fixes
Verify improvements with post-fix scans

Monitoring

Ongoing
Scheduled drift detection & compliance monitoring
Continuous AI-readiness scoring & alerting
ThunderScan Analysis

Sample Report

Real scorecards and auto-generated fix scripts for every finding.

Database Design Quality

Automated health checks ensuring referential integrity, structural normalization, and scalability.

80
score
Good
Design Health Score
Keys & Constraints
4 issues found
Normalization
2 warnings

Keys & Referential Integrity

Validates PK definitions and FK coverage to prevent orphaned records.

Normalization Health (1NF–3NF)

Identifies structural anti-patterns and redundant data creating technical debt.

Index Optimization

Flags missing indexes and removes unused duplicates based on query patterns.

Auto-Remediation Script

-- Suggestion: Fix missing FK constraint
ALTER TABLE orders
ADD CONSTRAINT fk_customer_id
FOREIGN KEY (customer_id)
REFERENCES customers(id)
ON DELETE CASCADE;

-- Fix: Add missing index for FK column
CREATE INDEX idx_orders_customer_id
ON orders(customer_id);

-- Fix: Normalize redundant column
ALTER TABLE orders
DROP COLUMN customer_name; -- use JOIN
All issues auto-fixable

Review, approve, and execute production-ready SQL fixes in one click.