performing-dmarc-policy-enforcement-rollout▌
mukul975/Anthropic-Cybersecurity-Skills · updated May 25, 2026
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Execute a phased DMARC rollout from p=none monitoring through p=quarantine to p=reject enforcement, ensuring all legitimate email sources are authenticated before blocking unauthorized senders.
| name | performing-dmarc-policy-enforcement-rollout |
| description | Execute a phased DMARC rollout from p=none monitoring through p=quarantine to p=reject enforcement, ensuring all legitimate email sources are authenticated before blocking unauthorized senders. |
| domain | cybersecurity |
| subdomain | phishing-defense |
| tags | - dmarc - spf - dkim - email-authentication - anti-spoofing - phishing - dns - email-security |
| version | '1.0' |
| author | mahipal |
| license | Apache-2.0 |
| nist_csf | - PR.AT-01 - DE.CM-09 - RS.CO-02 - DE.AE-02 |
Performing DMARC Policy Enforcement Rollout
Overview
Domain-based Message Authentication, Reporting and Conformance (DMARC) is the cornerstone of email anti-spoofing protection. A DMARC rollout progresses through three phases: monitoring (p=none), quarantine (p=quarantine), and full enforcement (p=reject). When configured at p=reject, any email that fails both SPF and DKIM checks is outright rejected. Google and Yahoo now require DMARC for bulk senders (5,000+ emails), driving a 65% reduction in unauthenticated messages. The rollout typically takes 3-6 months for safe deployment.
When to Use
- When conducting security assessments that involve performing dmarc policy enforcement rollout
- When following incident response procedures for related security events
- When performing scheduled security testing or auditing activities
- When validating security controls through hands-on testing
Prerequisites
- Administrative access to DNS management for the domain
- Understanding of SPF, DKIM, and DMARC protocols (RFC 7208, 6376, 7489)
- Complete inventory of all legitimate email sending sources
- DMARC reporting analysis tool (EasyDMARC, DMARCLY, Valimail, or dmarcian)
- Email gateway with DMARC enforcement capability
Key Concepts
DMARC Policy Levels
| Policy | Behavior | Use Case |
|---|---|---|
| p=none | Monitor only, no action on failures | Discovery phase |
| p=quarantine | Send failing messages to spam/junk | Transition phase |
| p=reject | Block failing messages entirely | Full enforcement |
DMARC Record Anatomy
v=DMARC1; p=quarantine; pct=25; rua=mailto:[email protected]; ruf=mailto:[email protected]; adkim=r; aspf=r; fo=1
- p: Policy for organizational domain
- sp: Policy for subdomains
- pct: Percentage of messages subject to policy (for gradual rollout)
- rua: Aggregate report destination (daily XML reports)
- ruf: Forensic report destination (per-failure reports)
- adkim: DKIM alignment mode (r=relaxed, s=strict)
- aspf: SPF alignment mode (r=relaxed, s=strict)
- fo: Failure reporting options (0=both fail, 1=either fails)
SPF and DKIM Alignment
- SPF Alignment: The domain in the Return-Path (envelope sender) must match the From header domain
- DKIM Alignment: The d= domain in the DKIM signature must match the From header domain
- Relaxed: Organizational domain match (sub.example.com matches example.com)
- Strict: Exact domain match required
Workflow
Step 1: Inventory All Sending Sources (Week 1-2)
- Audit all systems sending email as your domain (marketing, CRM, ticketing, transactional)
- Document third-party services: Salesforce, Mailchimp, SendGrid, Zendesk, etc.
- Identify internal mail servers, applications, and relay hosts
- Check for shadow IT email sending (departments using unauthorized services)
Step 2: Configure SPF and DKIM (Week 2-4)
- Consolidate SPF record with all legitimate sending IPs and includes
- Ensure SPF record stays under 10 DNS lookup limit
- Generate and publish DKIM keys for each sending source
- Verify DKIM signing works for all outbound mail paths
- Test with MX Toolbox or dmarcian SPF/DKIM validators
Step 3: Deploy DMARC in Monitoring Mode (Week 4-6)
- Publish initial DMARC record:
v=DMARC1; p=none; rua=mailto:[email protected]; fo=1 - Wait 1-2 weeks to collect representative aggregate reports
- Analyze reports to identify unauthorized senders and alignment failures
- Fix SPF/DKIM for all legitimate sources showing failures
- Iterate until all legitimate mail passes DMARC
Step 4: Move to Quarantine with pct Tag (Week 6-12)
- Update to quarantine at 10%:
v=DMARC1; p=quarantine; pct=10; rua=... - Monitor for false positives (legitimate mail being quarantined)
- Increase pct gradually: 10% -> 25% -> 50% -> 75% -> 100%
- Each increase: wait 1-2 weeks and review reports before advancing
- Fix any remaining alignment issues discovered at each stage
Step 5: Advance to Reject Policy (Week 12-20)
- After stable quarantine at 100%, move to reject at 10%:
v=DMARC1; p=reject; pct=10; rua=... - Gradually increase pct: 10% -> 25% -> 50% -> 100%
- Monitor closely for legitimate mail being rejected
- Establish emergency rollback procedure (revert to quarantine)
- Apply subdomain policy:
sp=rejectfor subdomains
Step 6: Ongoing Monitoring and Maintenance
- Continuously monitor DMARC aggregate reports
- Add new sending sources before they start sending
- Review forensic reports for spoofing attempts
- Maintain SPF record as sending infrastructure changes
- Rotate DKIM keys annually
Tools & Resources
- EasyDMARC: DMARC monitoring dashboard with aggregate/forensic report analysis
- DMARCLY: SPF, DKIM, DMARC monitoring with auto-DNS updates
- dmarcian: DMARC deployment and management platform
- Valimail: Automated DMARC enforcement with hosted authentication
- MX Toolbox: DNS record lookup and DMARC validator
- Google Admin Toolbox: DMARC check and diagnostic tools
Validation
- DMARC record published and resolving correctly at _dmarc.domain.com
- All legitimate sending sources pass SPF and/or DKIM alignment
- Aggregate reports show >99% legitimate mail passing DMARC
- Spoofed messages from unauthorized senders are rejected
- No legitimate mail blocked after full p=reject enforcement
- Subdomain policy (sp=) also set to reject
How to use performing-dmarc-policy-enforcement-rollout on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add performing-dmarc-policy-enforcement-rollout
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches performing-dmarc-policy-enforcement-rollout from GitHub repository mukul975/Anthropic-Cybersecurity-Skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate performing-dmarc-policy-enforcement-rollout. Access the skill through slash commands (e.g., /performing-dmarc-policy-enforcement-rollout) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
Task Automation & Efficiency
Automate repetitive workflows and reduce manual effort
Example
Generate reports, summarize documents, draft communications
Save 3-5 hours per week on routine tasks
Knowledge Enhancement
Learn new skills, understand complex topics, get expert guidance
Example
Explain concepts, provide examples, suggest learning resources
Accelerate learning and skill development by 2x
Quality Improvement
Enhance output quality through reviews, suggestions, and refinements
Example
Review drafts, suggest improvements, catch errors
Improve work quality by 30-40% with less effort
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client with skill support
- ›Clear understanding of task or problem to solve
- ›Willingness to iterate and refine outputs
Time Estimate
15-45 minutes depending on use case complexity
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate into regular workflow if valuable
Common Pitfalls
- ⚠Expecting perfect results without iteration
- ⚠Not providing enough context in prompts
- ⚠Using skill for tasks outside its intended scope
- ⚠Accepting outputs without review and validation
Best Practices▌
✓ Do
- +Start with clear, specific prompts
- +Provide relevant context and constraints
- +Review and refine all outputs before using
- +Iterate to improve output quality
- +Document successful prompt patterns
✗ Don't
- −Don't use without understanding skill limitations
- −Don't skip validation of outputs
- −Don't share sensitive information in prompts
- −Don't expect skill to replace human judgment
💡 Pro Tips
- ★Be specific about desired format and style
- ★Ask for multiple options to choose from
- ★Request explanations to understand reasoning
- ★Combine AI efficiency with human expertise
When to Use This▌
✓ Use When
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid When
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
Learning Path▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★30 reviews- ★★★★★Arjun Bhatia· Dec 12, 2024
performing-dmarc-policy-enforcement-rollout is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ganesh Mohane· Dec 8, 2024
performing-dmarc-policy-enforcement-rollout fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Xiao Farah· Dec 4, 2024
Keeps context tight: performing-dmarc-policy-enforcement-rollout is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Sakshi Patil· Nov 27, 2024
Registry listing for performing-dmarc-policy-enforcement-rollout matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Arjun Chawla· Nov 23, 2024
performing-dmarc-policy-enforcement-rollout has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Sophia Wang· Nov 3, 2024
Solid pick for teams standardizing on skills: performing-dmarc-policy-enforcement-rollout is focused, and the summary matches what you get after install.
- ★★★★★Tariq Li· Oct 22, 2024
performing-dmarc-policy-enforcement-rollout has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Chaitanya Patil· Oct 18, 2024
performing-dmarc-policy-enforcement-rollout reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Chinedu Tandon· Oct 14, 2024
Solid pick for teams standardizing on skills: performing-dmarc-policy-enforcement-rollout is focused, and the summary matches what you get after install.
- ★★★★★Piyush G· Sep 25, 2024
I recommend performing-dmarc-policy-enforcement-rollout for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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