Project 15: Email Authentication Dashboard

Build a web dashboard that evaluates a domain’s SPF, DKIM, and DMARC configuration with recommendations.

Quick Reference

Attribute	Value
Difficulty	Advanced
Time Estimate	2-3 weeks
Language	Python (Flask/FastAPI) (Alternatives: Go, Node.js)
Prerequisites	DNS queries, web development basics
Key Topics	Integrated auth checks, UX reporting, caching

1. Learning Objectives

Integrate SPF, DKIM, and DMARC checks into a single report.
Build a web UI that explains results clearly.
Implement caching to reduce DNS load.
Provide actionable recommendations for configuration improvements.

2. Theoretical Foundation

2.1 Core Concepts

Authentication chain: SPF and DKIM feed into DMARC.
Selector discovery: DKIM keys are stored at selector._domainkey.
Alignment: DMARC depends on alignment with From domain.
Explainability: Results must be clear for non-expert users.

2.2 Why This Matters

Most organizations misconfigure email authentication. A dashboard bridges the gap between technical records and actionable guidance.

2.3 Historical Context / Background

Email authentication has grown from SPF to DKIM to DMARC. Tooling is still fragmented and requires integration.

2.4 Common Misconceptions

Misconception: A single SPF record is always enough. Reality: SPF can be valid but too permissive.
Misconception: DKIM is present if a selector exists. Reality: Key format and size matter.

3. Project Specification

3.1 What You Will Build

A web application that accepts a domain, performs DNS queries for SPF/DKIM/DMARC, validates them, and displays a score with recommendations.

3.2 Functional Requirements

Input domain and run SPF, DKIM selector checks, and DMARC validation.
Display results in a dashboard with pass/fail indicators.
Provide recommendations (e.g., use -all, add DMARC policy).
Cache results with TTL to reduce repeated queries.

3.3 Non-Functional Requirements

Performance: Render results in under 1 second after DNS.
Reliability: Handle DNS timeouts and missing records.
Usability: Clear UI and consistent scoring.

3.4 Example Usage / Output

Browser: http://localhost:5000/check/example.com

SPF: PASS (v=spf1 include:_spf.example.com -all)
DKIM: FOUND (selector1, selector2)
DMARC: PASS (p=reject)
Score: 92/100
Recommendations: tighten SPF if using ~all

3.5 Real World Outcome

You can audit domain authentication posture quickly and provide recommendations for deliverability and security.

4. Solution Architecture

4.1 High-Level Design

Web UI
  -> API Endpoint
    -> DNS Query Layer
      -> SPF/DKIM/DMARC Evaluators
    -> Score Engine
    -> Cache

4.2 Key Components

Component	Responsibility	Key Decisions
DNS Layer	Query TXT and MX	Add timeout and retry
Evaluators	SPF/DKIM/DMARC parsing	Reuse existing logic
Score Engine	Compute score	Transparent weighting
UI	Display results	Show raw records and hints

4.3 Data Structures

class DomainReport:
    def __init__(self, domain, spf, dkim, dmarc, score, hints):
        self.domain = domain
        self.spf = spf
        self.dkim = dkim
        self.dmarc = dmarc
        self.score = score
        self.hints = hints

4.4 Algorithm Overview

Key Algorithm: Scoring

Start at 100.
Deduct for missing SPF/DKIM/DMARC.
Deduct for weak policy (~all or p=none).
Output score and reasons.

Complexity Analysis:

Time: O(n) for DNS lookups
Space: O(1)

5. Implementation Guide

5.1 Development Environment Setup

python -m venv .venv
source .venv/bin/activate
python -m pip install flask dnspython

5.2 Project Structure

auth-dashboard/
├── app.py
├── dns.py
├── evaluators/
│   ├── spf.py
│   ├── dkim.py
│   └── dmarc.py
├── templates/
└── static/

5.3 The Core Question You’re Answering

“Is this domain configured for modern email authentication, and what should be improved?”

5.4 Concepts You Must Understand First

Stop and research these before coding:

SPF syntax and lookup limits
DKIM selectors and key sizes
DMARC policy tags
DNS TTL and caching

5.5 Questions to Guide Your Design

How will you choose which DKIM selectors to test?
How will you score a domain with partial auth?
What is your caching strategy and TTL?

5.6 Thinking Exercise

If SPF passes but DMARC is missing, what should the dashboard score and recommend?

5.7 The Interview Questions They’ll Ask

“How do SPF, DKIM, and DMARC relate?”
“What is a good DMARC policy for a mature domain?”
“Why is caching essential for DNS-heavy tools?”

5.8 Hints in Layers

Hint 1: Reuse your SPF/DKIM/DMARC logic

Avoid reimplementing from scratch.

Hint 2: Provide raw DNS records

Let users see exact data.

Hint 3: Keep recommendations simple

Focus on clear, actionable changes.

5.9 Books That Will Help

Topic	Book	Chapter
DNS	TCP/IP Illustrated Vol 1	Ch. 11
Email auth	RFC 7208/6376/7489	key sections
Web basics	Flask docs	quickstart

5.10 Implementation Phases

Phase 1: Foundation (4-5 days)

Goals:

Build API and DNS layer

Tasks:

Create endpoint /check/.
Implement DNS TXT lookups.

Checkpoint: Returns raw records.

Phase 2: Core Functionality (1 week)

Goals:

Add evaluators and scoring

Tasks:

Parse and evaluate SPF/DKIM/DMARC.
Compute score and hints.

Checkpoint: Score and hints displayed.

Phase 3: Polish and Edge Cases (4-5 days)

Goals:

UI and caching

Tasks:

Add HTML templates and styling.
Implement TTL cache.

Checkpoint: Fast, usable dashboard.

5.11 Key Implementation Decisions

Decision	Options	Recommendation	Rationale
Framework	Flask vs FastAPI	Flask	Simpler for UI
Cache	in-memory vs Redis	in-memory	Easier setup
DKIM selectors	static list vs discovery	static list	Practical and fast

6. Testing Strategy

6.1 Test Categories

Category	Purpose	Examples
Unit Tests	SPF/DKIM/DMARC parsing	tag parsing
Integration Tests	Real domains	google.com
Edge Case Tests	Missing records	no DMARC

6.2 Critical Test Cases

Domain with no DMARC shows warning.
Weak SPF yields deduction.
DKIM missing reports selector not found.

6.3 Test Data

_dmarc.example.com TXT "v=DMARC1; p=reject"

7. Common Pitfalls and Debugging

7.1 Frequent Mistakes

Pitfall	Symptom	Solution
DNS rate limits	errors	cache and throttle
Misparsed TXT	wrong results	join TXT chunks
Overly strict scoring	low scores	adjust weights

7.2 Debugging Strategies

Display raw TXT records.
Compare with external tools.

7.3 Performance Traps

Re-querying DNS on every page load. Use TTL cache.

8. Extensions and Challenges

8.1 Beginner Extensions

Add JSON API output.
Add exportable reports.

8.2 Intermediate Extensions

Implement selector discovery by brute force.
Add MTA-STS and TLS-RPT checks.

8.3 Advanced Extensions

Build multi-domain scanning and alerts.
Add historical score tracking.

9. Real-World Connections

9.1 Industry Applications

Security teams audit domain authentication.
Deliverability teams track changes and regressions.

dmarcian and mxtoolbox for comparisons

9.3 Interview Relevance

Integrated auth evaluation and reporting are common in email security roles.

10. Resources

10.1 Essential Reading

RFC 7208 - SPF
RFC 6376 - DKIM
RFC 7489 - DMARC

10.2 Video Resources

Email authentication overview sessions

10.3 Tools and Documentation

dnspython docs

11. Self-Assessment Checklist

11.1 Understanding

I can explain SPF/DKIM/DMARC roles
I understand selector discovery
I can design a scoring system

11.2 Implementation

Dashboard renders results correctly
DNS caching works
Recommendations are actionable

11.3 Growth

I can extend to other email security standards
I can communicate findings to non-technical users

12. Submission / Completion Criteria

Minimum Viable Completion:

Show SPF, DKIM, DMARC status for a domain

Full Completion:

Provide score and recommendations

Excellence (Going Above and Beyond):

Add monitoring and historical tracking

This guide was generated from EMAIL_SYSTEMS_DEEP_DIVE_PROJECTS.md. For the complete learning path, see the parent directory.

Project 15: Email Authentication Dashboard

Quick Reference

1. Learning Objectives

2. Theoretical Foundation

2.1 Core Concepts

2.2 Why This Matters

2.3 Historical Context / Background

2.4 Common Misconceptions

3. Project Specification

3.1 What You Will Build

3.2 Functional Requirements

3.3 Non-Functional Requirements

3.4 Example Usage / Output

3.5 Real World Outcome

4. Solution Architecture

4.1 High-Level Design

4.2 Key Components

4.3 Data Structures

4.4 Algorithm Overview

5. Implementation Guide

5.1 Development Environment Setup

5.2 Project Structure

5.3 The Core Question You’re Answering

5.4 Concepts You Must Understand First

5.5 Questions to Guide Your Design

5.6 Thinking Exercise

5.7 The Interview Questions They’ll Ask

5.8 Hints in Layers

5.9 Books That Will Help

5.10 Implementation Phases

Phase 1: Foundation (4-5 days)

Phase 2: Core Functionality (1 week)

Phase 3: Polish and Edge Cases (4-5 days)

5.11 Key Implementation Decisions

6. Testing Strategy

6.1 Test Categories

6.2 Critical Test Cases

6.3 Test Data

7. Common Pitfalls and Debugging

7.1 Frequent Mistakes

7.2 Debugging Strategies

7.3 Performance Traps

8. Extensions and Challenges

8.1 Beginner Extensions

8.2 Intermediate Extensions

8.3 Advanced Extensions

9. Real-World Connections

9.1 Industry Applications

9.2 Related Open Source Projects

9.3 Interview Relevance

10. Resources

10.1 Essential Reading

10.2 Video Resources

10.3 Tools and Documentation

10.4 Related Projects in This Series

11. Self-Assessment Checklist

11.1 Understanding

11.2 Implementation

11.3 Growth

12. Submission / Completion Criteria