Content Licensing Signals

We're checking whether your site declares machine-readable reuse permissions that AI systems can parse programmatically. Four layers of licensing metadata get examined: Schema.org CreativeWork license properties in JSON-LD, HTML meta tags for copyright and licensing (<meta name="rights"> and <meta name="dc.rights">), visible copyright notices in page content, and HTTP headers related to content licensing (like X-Robots-Tag with licensing directives).

The primary metric is "licensing signal coverage" -the percentage of content pages with at least one machine-readable licensing signal. A secondary metric, "licensing clarity," checks whether signals are unambiguous. A page with a Creative Commons license URL in JSON-LD and an "All Rights Reserved" notice in the footer? Those are contradictory signals that AI systems can't resolve.

We also check for the Schema.org license property within Article, BlogPosting, and CreativeWork types. This property accepts a URL pointing to the license terms -a Creative Commons deed, a custom terms page, whatever. Pages with a license property give AI systems a definitive, machine-parseable answer to: "Can I use this content?"

The audit evaluates whether licensing is consistent site-wide or varies per page. Some sites appropriately use different licenses for different content types -CC BY-SA for blog posts, All Rights Reserved for proprietary research. We flag inconsistencies that look accidental (identical blog posts with different licenses) while noting intentional per-section policies.

AI systems are getting cautious about content reuse. As legal frameworks around AI training and retrieval evolve, AI companies are building systems that respect licensing signals. Content with clear, permissive licensing is more likely to be quoted, summarized, and cited -because the AI can verify it has permission.

When your site lacks licensing signals entirely, AI systems default to conservative behavior. Without a clear signal that content can be referenced, some AI systems choose not to cite it at all -or cite it with less detail than they would for clearly licensed content. This is the opposite of what most publishers want. They want extensive citation with attribution, but they haven't told the AI systems that's acceptable.

The strategic approach depends on your business model. Publishers wanting maximum AI visibility should use permissive signals (Creative Commons Attribution or similar) that explicitly allow quotation with attribution. Sites wanting to prevent AI reuse should use restrictive signals. The worst outcome is no signal at all -AI systems are left guessing, and different systems guess differently.

Site-wide consistency matters because AI systems evaluate trust at the domain level. A site where 80% of pages have clear licensing and 20% have contradictory or missing licensing creates uncertainty. Consistent licensing across all pages -even a standard copyright notice in structured data -signals a deliberate content policy.

We extract licensing information from every crawled content page through four detection methods running in parallel.

Method one: JSON-LD parsing for the license property on any CreativeWork subtype (Article, BlogPosting, WebPage, Product). Valid values are URLs pointing to recognized license deeds -Creative Commons, MIT, GNU, or custom terms pages. We validate that the license URL is accessible (returns 200) and identify the license type.

Method two: HTML meta tags. We look for <meta name="rights">, <meta name="dc.rights">, <meta name="dcterms.license">, and <meta name="copyright"> tags. Also <link rel="license"> elements in the document head, which some CMS platforms generate automatically.

Method three: visible page content scan using pattern matching. We identify strings matching common copyright patterns: "© 2026 Company Name", "Copyright 2026", "All Rights Reserved", "Licensed under CC BY", and similar. Visible notices aren't machine-readable the same way structured data is, but their presence still counts as a minimal signal.

Method four: HTTP response headers for X-Robots-Tag with licensing info and the emerging TDM-Reservation header from the EU's DSM Directive implementation.

Then we cross-validate all signals for consistency. A page with CC BY-SA in JSON-LD, "All Rights Reserved" in a meta tag, and no visible copyright notice gets a consistency penalty. The output is a licensing matrix showing which signal types are present on which page templates -making template-level gaps easy to spot.

Licensing scoring uses a three-component rubric:

1. Licensing signal presence (4 points): - 80%+ of content pages have at least one machine-readable signal (JSON-LD license, meta tag, or link rel="license"): 4/4 points - 60-79% coverage: 3/4 points - 40-59% coverage: 2/4 points - 20-39% coverage with at least visible copyright notices: 1/4 points - No licensing signals detected: 0/4 points

2. Signal quality and machine-readability (3 points): - JSON-LD license property with valid, accessible URL on majority of pages: 3/3 points - HTML meta tags or link rel="license" without JSON-LD: 2/3 points - Only visible copyright text, no structured licensing data: 1/3 points - No parseable licensing information at all: 0/3 points

3. Consistency and clarity (3 points): - All pages use the same approach, no contradictions: 3/3 points - Intentional per-section variation, no contradictions within sections: 2.5/3 points - Minor inconsistencies -less than 10% conflicting: 2/3 points - Significant contradictions -different license types on similar content: 1/3 points - Contradictory signals on the same page (CC BY and All Rights Reserved): 0/3 points

Bonus: - +0.5 points if the site has a dedicated licensing/terms page linked from JSON-LD license properties

Deductions: - -1 point if license URLs in JSON-LD return 404 or errors - -0.5 points if licensing signals exist only in JavaScript-rendered HTML

This is a newer criterion. Most sites currently score 1-4. Sites with deliberate AI visibility strategies and proper Schema.org implementation score 7-10.