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MCP Tools Reference

Once configured, the GESF MCP server provides 43 tools to your AI assistant. This page documents every tool with exact prompts you can copy and paste into your AI assistant.

Tool Overview

Compliance Assessment

Tool Description Parameters
check_compliance Check compliance status for a project type project_type (string)
check_project_status Read actual .ges/ directory for real-time status project_path (string)
list_missing_controls Show controls that are not passing framework (string)
list_framework_controls List all controls for a framework framework (string), status_filter (string)
run_audit Run a full 6-scanner source code audit project_path (string)
generate_compliance_report Full compliance report with scoring project_name (string), project_type (string), frameworks (string)
generate_audit_report Audit findings + compliance scoring project_name (string), project_path (string)

Fix & Implement

Tool Description Parameters
auto_fix Automatically fix security findings project_path (string), dry_run (string), rule_ids (string)
implement_control Generate implementation files for a control control_id (string), project_path (string)
apply_control_override Mark a control as not-applicable or pass control_id (string), project_path (string), status (string), reason (string)
fix_recommendation Step-by-step remediation guidance control_id (string), finding_title (string)

Document Generation

Tool Description Parameters
generate_retention_policy Generate a data retention policy project_name (string)
generate_incident_response Generate an incident response plan project_name (string)
generate_risk_assessment Generate a risk assessment project_name (string)
generate_dpa Generate a Data Processing Agreement project_name (string)
generate_data_inventory Generate a data inventory project_name (string), project_type (string)
generate_processing_records Generate Article 30 ROPA project_name (string), controller_name (string)

Governance Provenance

Tool Description Parameters
create_governance_record Create a governance provenance record project_path (string), system_name (string), system_type (string), risk_level (string)
approve_governance_record Record an approval decision project_path (string), record_id (string), approver_name (string), decision (string)
add_governance_evidence Attach an evidence reference project_path (string), record_id (string), title (string), source_system (string), reference (string)
list_governance_records List all governance records project_path (string)
get_governance_record Get full provenance chain project_path (string), record_id (string)
verify_governance_record Verify provenance completeness project_path (string), record_id (string)
set_governance_risk_assessment Link a risk assessment project_path (string), record_id (string), assessor (string), methodology (string), risk_score (string)
set_governance_policy_basis Document policy basis project_path (string), record_id (string), policy_name (string), standard (string)
set_governance_review_cycle Set periodic review project_path (string), record_id (string), frequency (string), next_review (string)
set_governance_data_inventory Document data inventory project_path (string), record_id (string)
set_governance_committee Record committee approval project_path (string), record_id (string)
set_governance_compliance_links Map compliance frameworks project_path (string), record_id (string), frameworks (string)

See the Governance guide for the full provenance chain workflow.

check_compliance

Returns the compliance score for a given project type, showing which policy packs apply and their control status.

Parameters:

Parameter Type Required Description
project_type string No Project type (default: saas)

Valid project_type values:

saas, ai-application, mcp-server, blockchain, wallet, government-system, healthcare-system, event-platform, photo-storage-platform, vulnerability-scanner, generic-web-application, api-backend, mobile-application

Prompts to Try

Are we GDPR compliant?

The assistant calls check_compliance with project_type: "saas" (default).

Check compliance status for a healthcare application.

The assistant calls check_compliance with project_type: "healthcare-system".

I'm building an AI application. What's our compliance posture?

The assistant calls check_compliance with project_type: "ai-application".

We have a blockchain wallet project. Check our compliance.

The assistant calls check_compliance with project_type: "wallet".

Example response:

  GDPR ................ 72%
  OWASP ............... 65%
  CIS ................. 80%
  NIST ................ 58%
  Overall ............. 69%

list_missing_controls

Shows compliance controls that are not in a passing state for a given framework.

Parameters:

Parameter Type Required Description
framework string No Framework name (default: GDPR)

Valid framework values: GDPR, OWASP, CIS, NIST

Prompts to Try

Show me missing GDPR controls.

The assistant calls list_missing_controls with framework: "GDPR".

What OWASP controls are we failing?

The assistant calls list_missing_controls with framework: "OWASP".

List missing CIS controls for our project.

The assistant calls list_missing_controls with framework: "CIS".

What NIST CSF controls do we need to implement?

The assistant calls list_missing_controls with framework: "NIST".

Example response:

- [CRITICAL] GDPR-ART32-002: Encryption at Rest
  Implement AES-256-GCM encryption for all stored personal data.

- [CRITICAL] GDPR-ART32-006: Audit Logging
  Implement audit trail for all access to personal data.

- [HIGH] GDPR-ART32-005: Automatic Session Timeout
  Configure automatic session expiration after 30 minutes of inactivity.

- [HIGH] GDPR-ART5-005: Storage Limitation
  Implement a data retention policy with defined retention periods.

generate_retention_policy

Generates a data retention policy template with standard retention periods aligned to GDPR Article 5(1)(e) storage limitation principle.

Parameters:

Parameter Type Required Description
project_name string No Your project name (default: Project)

Prompts to Try

Generate a retention policy for My SaaS App.

We're building a SaaS platform called "CloudMetrics" that stores
user data, billing records, and audit logs. Generate a retention
policy that covers all these data categories.

Our GDPR compliance check showed we're missing a retention policy.
Generate one for our project "DataVault".

What the generated policy includes:

  • Purpose and scope
  • Data categories with specific retention periods
  • Disposal procedures
  • Legal basis references
  • Review schedule

generate_incident_response

Generates an incident response plan template with severity levels, response procedures, and GDPR breach notification timelines (72-hour requirement from Article 33).

Parameters:

Parameter Type Required Description
project_name string No Your project name (default: Project)

Prompts to Try

Generate an incident response plan for Project Alpha.

Our healthcare platform "MedTrack" handles patient data.
Create an incident response plan that covers GDPR breach
notification requirements and HIPAA considerations.

We need to prepare for potential data breaches. Generate
a comprehensive incident response plan for "SecurePay"
that includes the 72-hour GDPR notification timeline.

What the generated plan includes:

  • Severity classification (Critical, High, Medium, Low)
  • Response team roles and responsibilities
  • Detection and triage procedures
  • Containment and eradication steps
  • GDPR Article 33 notification timeline (72 hours)
  • GDPR Article 34 data subject communication
  • Post-incident review template

generate_risk_assessment

Generates a risk assessment template with common risk categories, likelihood/impact ratings, and mitigation strategies.

Parameters:

Parameter Type Required Description
project_name string No Your project name (default: Project)

Prompts to Try

Generate a risk assessment for our healthcare platform.

We process payment card data and personal information in
our e-commerce platform "ShopSecure". Generate a risk
assessment covering data breach, unauthorized access, and
compliance violation scenarios.

Our compliance check showed several failing controls.
Generate a risk assessment for "DataVault" that accounts
for our current compliance gaps.

What the generated assessment includes:

  • Risk register with categories (operational, technical, compliance, legal)
  • Likelihood and impact ratings (1-5 scale)
  • Risk score matrix
  • Mitigation strategies per risk
  • Residual risk acceptance
  • Review schedule

generate_dpa

Generates a Data Processing Agreement template based on GDPR Article 28 requirements. A DPA is the legally required contract between a data controller and data processor.

Parameters:

Parameter Type Required Description
project_name string No Your project name (default: Project)

Prompts to Try

Generate a Data Processing Agreement for Customer Portal.

We're hiring a cloud provider to process customer data for
"DataVault". Generate a DPA that covers Article 28 requirements
including sub-processor management and data deletion obligations.

Generate a DPA for our platform "CloudMetrics" where we act
as both a data controller (for our own users) and a data
processor (for our enterprise customers' data).

What the generated DPA includes:

  • Parties and roles (controller, processor, sub-processor)
  • Processing scope and purposes
  • Data subject rights handling
  • Security measures (Article 32)
  • Breach notification obligations
  • Sub-processor management
  • Data deletion and return
  • Audit rights
  • Liability and indemnification

check_project_status

Reads the actual .ges/ directory in your project to report real-time compliance status, scores, configuration, and overrides. Unlike check_compliance (which uses templates), this tool reads your project's actual files.

Parameters:

Parameter Type Required Description
project_path string Yes Absolute path to the project root

Prompts to Try

What's the current compliance status of my project?

The assistant calls check_project_status with project_path set to your project directory.

I just ran ges audit. Show me the updated project status.

What's our current compliance score from the .ges directory?

What the response includes:

  • Project name, type, and configured frameworks
  • Current compliance scores from .ges/score.json
  • Active control overrides from .ges/control-overrides.json
  • Framework version

list_framework_controls

Lists all controls for a given framework with their status, severity, category, and implementation guidance.

Parameters:

Parameter Type Required Description
framework string Yes Framework name: GDPR, OWASP, CIS, NIST
status_filter string No Filter by status: pass, fail, warning, not-implemented, not-applicable

Prompts to Try

List all GDPR controls with their status.

Show me only the failing OWASP controls.

The assistant calls list_framework_controls with framework: "OWASP" and status_filter: "fail".

Give me a full breakdown of all NIST controls.

run_audit

Runs a full source code audit using 6 scanners (secrets, crypto, code security, authentication, configuration, database) against your project. Returns findings with severity, file locations, evidence, and fix suggestions.

Parameters:

Parameter Type Required Description
project_path string Yes Absolute path to the project root

Prompts to Try

Run a security audit on /path/to/my/project.

Scan my project for security issues and compliance problems.

Audit /path/to/my/project before we deploy. Show me all critical
and high severity findings.

What the audit checks:

Scanner What It Detects
Secrets Scanner Hardcoded passwords, API keys, connection strings, private keys, JWTs
Cryptographic Scanner MD5, SHA1, weak AES, disabled TLS verification
Code Security Scanner SQL injection, XSS, eval/code injection
Authentication Scanner Routes without auth, missing rate limiting, no session timeout, wildcard CORS, no MFA
Configuration Scanner Missing helmet/CORS, secrets in .env, Docker as root, missing .gitignore, no logging
Database Scanner Missing timestamps, soft delete, user audit columns, no audit model

generate_compliance_report

Generates a comprehensive compliance report with executive summary, framework scores, risk assessment, and recommendations — without scanning source code.

Parameters:

Parameter Type Required Description
project_name string No Project name for the report title
project_type string No Project type (default: generic-web-application)
frameworks string No Comma-separated framework names (default: GDPR,OWASP,CIS,NIST)

Prompts to Try

Generate a full compliance report for my SaaS application "CloudMetrics"
covering GDPR, OWASP, CIS, and NIST.

Generate a GDPR compliance report for our healthcare platform "MedTrack".

Create an executive compliance summary for our blockchain wallet project.

generate_audit_report

Combines real source code audit findings with compliance scoring into a single report. This is the most comprehensive report — it both scans your code and evaluates compliance.

Parameters:

Parameter Type Required Description
project_name string No Project name for the report
project_path string Yes Absolute path to the project root

Prompts to Try

Audit /path/to/my/project and generate a complete audit report
with findings and compliance scoring.

We have a GDPR audit next week. Run a full audit on /path/to/project
and generate an audit report we can present.

auto_fix

Scans your project for security findings and automatically applies fixes to source code files. Supports dry-run mode to preview changes before applying them. Covers 15 rule types across 7 programming languages.

Parameters:

Parameter Type Required Description
project_path string Yes Absolute path to the project root
dry_run string No "true" to preview changes without applying (recommended first)
rule_ids string No Comma-separated rule IDs to fix (e.g., CONFIG-001,AUTH-002). Omit to fix all.

What Auto-Fix Can Fix (15 Rule Types)

Rule Fix Applied
CONFIG-001 Security headers middleware (helmet/django-csp/tracing)
CONFIG-002 CORS configuration with environment-based origins
CONFIG-004 Add .env to .gitignore
CONFIG-005 Add non-root USER to Dockerfile
CONFIG-007 Re-enable TLS verification
CONFIG-008 Create .gitignore with security entries
CONFIG-009 Add missing entries to .gitignore
CONFIG-010 Create logger with structured audit logging
SECRETS-001 Extract hardcoded secrets to .env, replace with env vars
CRYPTO-001 Replace MD5/SHA1 with SHA-256
CRYPTO-003 Create Argon2id password hashing utility
AUTH-002 Add rate limiting middleware
AUTH-003 Add session timeout configuration
AUTH-004 Replace CORS wildcard with environment-configured origins
DB-001/002/003 Add audit columns (timestamps, soft delete, user audit)
DB-004 Add Audit model/table

Supported Languages

Language Frameworks
JavaScript/TypeScript Express, Fastify, Koa, Hono, Next, NestJS, SvelteKit
Python Django, Flask, FastAPI, Sanic
Go Gin, Fiber, Echo, Chi, Gorilla, net/http
Java Spring, Ktor, Quarkus, Micronaut
Ruby Rails, Sinatra
PHP Laravel, Symfony, Slim, Lumen
Rust Actix-web, Axum, Rocket, Warp

Prompts to Try

Run a dry-run auto-fix on /path/to/my/project. Show me what
would change without actually modifying anything.

The assistant calls auto_fix with dry_run: "true".

Scan /path/to/my/project and auto-fix all security issues
you can fix automatically.

Fix only the secrets and rate limiting issues in /path/to/my/project.

The assistant calls auto_fix with rule_ids: "SECRETS-001,AUTH-002".

My Express.js app at /path/to/app has no security headers,
no CORS config, and hardcoded secrets. Auto-fix everything.

Scan my Actix-web project at /path/to/rust-app and auto-fix
what you can.

First, dry-run auto-fix on /path/to/my/project. Then, if it
looks safe, apply the fixes.

The assistant calls auto_fix twice — first with dry_run: "true", then without.

What the response includes:

  • Total findings before fix
  • Auto-fixable findings count
  • Actions applied (file modifications, new files created)
  • Manual review items (issues that cannot be auto-fixed)
  • npm/cargo/pip install guidance for new dependencies

implement_control

Generates production-ready implementation files for a specific compliance control. Detects your project's language and framework, then generates appropriate code.

Parameters:

Parameter Type Required Description
control_id string Yes Control ID (e.g., GDPR-ART32-002, GDPR-ART32-004)
project_path string Yes Absolute path to the project root

Supported Controls

Control What Gets Generated
GDPR-ART32-002 (Encryption at Rest) AES-256-GCM encrypt/decrypt utility
GDPR-ART32-003 (Encryption in Transit) TLS/HTTPS redirect middleware
GDPR-ART32-004 (User Identification) Argon2id password hashing utility
GDPR-ART32-005 (Session Timeout) Session expiration configuration
GDPR-ART32-006 (Audit Logging) Structured audit logger
GDPR-ART32-007 (Integrity Controls) SHA-256 integrity verification
GDPR-ART32-008 (Backup & Recovery) Encrypted backup script
GDPR-ART32-009 (Security Testing) CI/CD security scan workflow

Prompts to Try

Implement encryption at rest for GDPR-ART32-002 in /path/to/my/project.

Generate a password hashing module for GDPR-ART32-004
in /path/to/my/project.

Create an audit logging module for my project at /path/to/my/project
to satisfy GDPR-ART32-006.

Implement security testing for GDPR-ART32-009 — generate
a CI/CD workflow for /path/to/my/project.

Implement GDPR-ART32-002, GDPR-ART32-004, and GDPR-ART32-006
for /path/to/my/project.

apply_control_override

Marks a compliance control as not-applicable or pass in .ges/control-overrides.json. This is useful when a control doesn't apply to your project or has been manually verified.

Parameters:

Parameter Type Required Description
control_id string Yes Control ID (e.g., GDPR-ART32-001)
project_path string Yes Absolute path to the project root
status string Yes New status: not-applicable or pass
reason string No Reason for the override

Prompts to Try

Mark GDPR-ART32-001 as not-applicable for /path/to/my/project
because we don't pseudonymize data — we don't store personal data at all.

Mark OWASP-ASVS-003 as pass for /path/to/my/project —
we manually verified our authentication implementation.

Mark GDPR-ART33-001 and GDPR-ART33-002 as not-applicable
for /path/to/my/project — we're an internal tool with no data subjects.

fix_recommendation

Provides step-by-step remediation guidance for a specific compliance control or finding. Gives implementation steps, code examples, and verification procedures.

Parameters:

Parameter Type Required Description
control_id string No Control ID (e.g., GDPR-ART32-002, OWASP-AUTH-001)
finding_title string No Title of a specific audit finding to get fix guidance for

Prompts to Try

How do I fix GDPR-ART32-002 (encryption at rest)?

I have a finding about hardcoded API keys. How do I fix it?

Give me step-by-step guidance to implement CIS-002
(software inventory / SBOM).

generate_data_inventory

Generates a data inventory document listing data categories, classifications, retention periods, and legal basis. Required for GDPR Article 30 compliance.

Parameters:

Parameter Type Required Description
project_name string No Project name
project_type string No Project type (affects data categories)

Prompts to Try

Generate a data inventory for our SaaS application "CloudMetrics".

We're building an AI application that processes user prompts and
generates content. Generate a data inventory covering all data
categories including training data and model outputs.

Generate a data inventory for our healthcare platform "MedTrack"
that includes patient data, medical records, and appointment data.

generate_processing_records

Generates Article 30 Records of Processing Activities (ROPA). Documents all processing activities, purposes, data categories, recipients, and retention periods.

Parameters:

Parameter Type Required Description
project_name string No Project name
controller_name string No Data controller organization name

Prompts to Try

Generate processing records (ROPA) for "CloudMetrics" with
controller "CloudMetrics Inc."

Generate Article 30 processing records for our platform "DataVault"
where we use AWS for hosting, Stripe for payments, and SendGrid
for emails. Controller is "DataVault GmbH".

Combining Tools — Real-World Prompt Patterns

AI assistants can call multiple tools in sequence. Here are compound prompts that chain tools together for real-world compliance workflows:

Pattern 1: New Project Compliance Kickoff

I'm starting a new SaaS platform called "CloudMetrics" that will
process EU user data. Check our compliance status, show what's
missing for GDPR, and generate a risk assessment.

The assistant will: 1. Call check_compliance with project_type: "saas" 2. Call list_missing_controls with framework: "GDPR" 3. Call generate_risk_assessment with project_name: "CloudMetrics"

Pattern 2: Pre-Audit Document Package

We have an upcoming GDPR audit for "DataVault". Generate all
the documents we'll need: retention policy, incident response
plan, risk assessment, and a DPA template.

The assistant will call all 4 generation tools with project_name: "DataVault".

Pattern 3: Gap Analysis and Remediation Plan

Check compliance for our healthcare application. Then show me
what's missing for both GDPR and NIST frameworks, and generate
a risk assessment that accounts for those gaps.

The assistant will: 1. Call check_compliance with project_type: "healthcare-system" 2. Call list_missing_controls with framework: "GDPR" 3. Call list_missing_controls with framework: "NIST" 4. Call generate_risk_assessment with project_name: "MedTrack"

Pattern 4: Vendor Onboarding

We're onboarding a new payment processor for "ShopSecure".
Check our compliance, generate a DPA, and create a risk
assessment for the vendor relationship.

Pattern 5: Incident Preparation

We had a near-miss security incident today. Check our current
compliance posture, generate an incident response plan, and
create a risk assessment for our platform "SecurePay".

Pattern 6: Auto-Fix Dry Run + Apply

Run a dry-run auto-fix on /path/to/my/project. Review the
proposed changes, then apply them. After that, generate an
audit report.

The assistant will: 1. Call auto_fix with dry_run: "true" 2. Call auto_fix without dry_run (applies fixes) 3. Call generate_audit_report to show the improved state

Pattern 7: Full Remediation Workflow

Audit /path/to/my/project, auto-fix everything you can,
implement the remaining GDPR encryption controls, then
generate a compliance report.

The assistant will: 1. Call run_audit to scan the project 2. Call auto_fix to fix what's auto-fixable 3. Call implement_control for controls that need manual implementation 4. Call generate_compliance_report to show the updated posture

Pattern 8: Gap Analysis + Fix + Override

Check compliance for our SaaS platform, show missing GDPR
controls, auto-fix the code issues, implement the encryption
control, and mark the pseudonymisation control as
not-applicable since we don't store raw personal data.

The assistant will: 1. Call check_compliance with project_type: "saas" 2. Call list_missing_controls with framework: "GDPR" 3. Call auto_fix on the project 4. Call implement_control for encryption 5. Call apply_control_override for pseudonymisation

Pattern 9: Pre-Deploy Security Gate

We're about to deploy /path/to/my/project. Run a full audit,
dry-run auto-fix to see what can be improved, show me the
critical findings that MUST be fixed before deploy, and
generate an audit report.

Pattern 10: New Project Full Setup

We're starting a new SaaS platform called "CloudMetrics".
Check our initial compliance status, show all missing controls
across GDPR, OWASP, CIS, and NIST, generate all policy
documents, and auto-fix our codebase for security issues.

Prompt Writing Tips

Good Prompts

Prompt Why It Works
"Check compliance for a SaaS application" Specific project type triggers accurate results
"Show missing GDPR controls" Explicit framework name avoids ambiguity
"Generate a DPA for DataVault" Project name personalizes the document
"Check compliance, show missing OWASP controls, and generate a risk assessment for CloudMetrics" Compound prompt chains multiple tools

Bad Prompts

Prompt Why It Fails
"Is everything ok?" Too vague — no compliance context
"Tell me about security" No specific question for a tool to answer
"Fix my code" Too vague — specify the project path and whether you want auto_fix or implement_control

Getting the Best Results

  1. Always name your project — Generated documents use the project name
  2. Specify the project type — "SaaS", "healthcare", "blockchain" — for accurate compliance checks
  3. Name the framework — "GDPR", "OWASP", "CIS", "NIST" — for targeted control analysis
  4. Chain requests — Ask for compliance check + gap analysis + document generation in one prompt
  5. Be specific about context — "We process healthcare data" produces better risk assessments than "generate a risk assessment"

Exercise: Test Every Tool Individually

In your AI assistant, try each tool one at a time. Record the response:

# Prompt Used Tool Called Response Quality (1-5)
1 "Are we GDPR compliant?"
2 "Show missing OWASP controls"
3 "Show missing NIST controls"
4 "Show missing CIS controls"
5 "Generate a retention policy for MyApp"
6 "Generate an incident response plan for MyApp"
7 "Generate a risk assessment for MyApp"
8 "Generate a DPA for MyApp"
9 "Generate a data inventory for MyApp"
10 "Generate processing records for MyApp"
11 "List all GDPR controls with status"
12 "Run an audit on /path/to/project"
13 "Generate a compliance report for MyApp"
14 "Dry-run auto-fix on /path/to/project"
15 "Implement GDPR-ART32-002 for /path/to/project"
16 "Mark GDPR-ART32-001 as not-applicable"
17 "How do I fix CONFIG-001?"

Questions

  • Which tool produces the most detailed response?
  • Which tool would be most useful during a real audit?
  • Did the assistant always call the correct tool, or did it sometimes answer without using a tool?

Exercise: Compound Prompt Progression

Start with a simple prompt, then add complexity. See how the assistant handles each:

Round 1 — Single tool: 

Check compliance for a SaaS application.

Round 2 — Two tools: 

Check compliance for a SaaS application called "CloudMetrics"
and show missing GDPR controls.

Round 3 — Three tools: 

Check compliance for a SaaS application called "CloudMetrics",
show missing GDPR controls, and generate a risk assessment.

Round 4 — Full workflow: 

I'm building a SaaS platform called "CloudMetrics".
Check our compliance, show missing controls for GDPR,
OWASP, and NIST, then generate a retention policy,
incident response plan, and DPA.

Observe: - How many tool calls does the assistant make in each round? - Does the assistant maintain context between calls? - Does the compound prompt produce better results than individual calls?

Exercise: Multi-Tool Workflow

Ask your AI assistant to perform a complete compliance check with a single prompt:

I'm building a healthcare application called "MedTrack".
Check compliance for healthcare-system type, list all
missing GDPR controls, and generate both a risk assessment
and a data processing agreement.

Observe how the assistant chains multiple tool calls together.

Exercise: Prompt Engineering Challenge

Try to trigger all 17 tools with a single prompt:

We're launching "SecureBank", a financial SaaS platform at
/path/to/project. We need a full compliance review: check our
status, show every missing control across all frameworks, run
a source code audit, dry-run auto-fix, generate all policy
documents (retention, incident response, risk assessment, DPA,
data inventory, processing records), and generate a full
audit report.

Count: - How many tool calls did the assistant make? - Were all 17 tools called? - Which tools were missed, if any?

Exercise: Wrong Project Type Experiment

Try checking compliance with different project types and compare:

Check compliance for a blockchain wallet project.

vs

Check compliance for a government system.

vs

Check compliance for a SaaS application.

Questions

  • How do the policy packs differ between project types?
  • Which project type has the most controls to satisfy?
  • Which controls are common across all project types?

Exercise: Document Quality Comparison

Generate the same document twice with different context:

Without context: 

Generate a risk assessment for MyApp.

With context: 

We run "MyApp", a photo storage platform that processes
biometric data (face detection), stores images in S3, uses
AI for image tagging, and serves EU customers. Generate
a risk assessment covering all these specific areas.

Compare the two outputs — does context improve the quality?

Exercise: Auto-Fix Workflow

This exercise tests the full audit → fix → verify workflow:

Step 1 — Audit: 

Run an audit on /path/to/my/project and show me all findings.

Step 2 — Preview fixes: 

Dry-run auto-fix on /path/to/my/project. Show me exactly what
would change without modifying anything.

Step 3 — Apply fixes: 

Apply auto-fix to /path/to/my/project. Fix only CONFIG-001,
CONFIG-002, AUTH-002, and SECRETS-001.

Step 4 — Implement remaining controls: 

Implement GDPR-ART32-002 (encryption), GDPR-ART32-006 (audit
logging), and GDPR-ART32-004 (user identification) for
/path/to/my/project.

Step 5 — Verify: 

Run a new audit on /path/to/my/project and generate an audit
report to see how the compliance score improved.

Questions

  • How many findings were there before vs after auto-fix?
  • Which findings required manual review vs were auto-fixed?
  • Did the compliance score improve after the fixes?
  • Which controls still need manual implementation?

run_inference

Run AI-powered inference on GESF compliance data. Analyzes audit findings (clustering + deduplication), identifies root causes via graph analysis, detects score anomalies, and predicts compliance trends.

Parameters:

Parameter Type Required Description
project_path string No Project root path (defaults to CWD)

Returns: Structured report with four sections — Finding Clustering, Root Cause Analysis, Score Anomalies, and Trend Predictions.

Example prompt:

Run AI inference on my project and tell me:
1. What is the root cause of most findings?
2. Are my compliance scores trending up or down?
3. Should I be concerned about any anomalies?

Example exercise:

For /path/to/my/project, run AI inference and interpret the results.
Which 3 actions would have the biggest impact on our compliance score?

See the AI Inference guide for full documentation with output examples and interactive exercises.

Exercise: Control Override Workflow

Practice marking controls that don't apply or have been manually verified:

For /path/to/my/project, mark these controls as not-applicable:
- GDPR-ART33-001 (breach notification to authority) — internal tool
- GDPR-ART34-001 (breach communication to subjects) — no data subjects
- GDPR-ART30-002 (processor records) — we are the sole processor

And mark GDPR-ART32-003 as pass — we enforce TLS 1.3 everywhere.

Then verify: 

Check the real-time project status for /path/to/my/project
and show me which controls have overrides.