Guide: How to Audit Your Site for Being Used in AI Answers and Knowledge Bases

Hook: Why your site might be feeding AI answers — and losing attribution

If your WordPress knowledge base or documentation shows up in an AI answer without clear attribution or permission, you’re not just losing traffic — you may be losing revenue, brand control, and legal rights. In 2026, AI answer surfaces (generative search boxes, chat assistants, and knowledge graphs) increasingly pull from the open web and data marketplaces. That makes it critical to audit both your entity signals and your machine-readable provenance so your site appears correctly in AI answers — with the attribution and consent you require.

Top takeaway (inverted pyramid): What to do right now

1. Inventory and map every knowledge-base page to an entity (Wikidata/QID or internal canonical).
2. Publish machine-readable provenance and licensing metadata (JSON-LD + /.well-known) for every article.
3. Use schema.org types (TechArticle, FAQPage, HowTo, Dataset) + author/publisher/license/datePublished.
4. Signal snippet and training controls via meta tags, data-nosnippet, and X-Robots-Tag headers where needed.
5. Monitor AI-answer surfaces, log anomalies, and register content in licensing marketplaces where appropriate (e.g., post-2025 data marketplaces).

Why this matters in 2026: trends shaping AI visibility and provenance

Late 2025 through early 2026 saw three shifts that make provenance and entity SEO essential:

• AI systems increasingly credit sources when they can — and some platforms require machine-readable provenance to provide proper attribution.
• Content licensing marketplaces (highlight: Cloudflare’s acquisition of Human Native in 2025) accelerated commercial models for content training and attribution. See how creator commerce and marketplace strategies are changing content ownership in Creator Commerce SEO & Story‑Led Rewrite Pipelines (2026).
• Search and social channels are converging; audiences form brand preferences before they search, so consistent entity signals across platforms matter more than single-page rank.

“Discoverability now means showing up consistently across the touchpoints that make decisions — search, social, and AI answers.” — industry summary, 2026

Audit Overview: Two-track approach (Entity SEO + Provenance checks)

Combine an entity-first SEO audit (mapping content to knowledge graph nodes) with a provenance audit (machine-readable proof of authorship, license, and consent). Treat them as parallel tracks that meet at structured data, canonical URLs, and monitoring.

Track A — Entity SEO Audit (make your content intelligible to AI)

Goal: Make every article unambiguously about the right entity so AI answers attribute and rank it correctly.

1. Inventory pages and define primary entity
   • Export a sitemap or run wp-cli or Screaming Frog to list all KB pages.
   • For each page, assign a primary entity: product, feature, person, concept, or company. Use Wikidata QIDs where possible.
2. Publish clear entity markup
   • Add JSON-LD 'about' or 'mainEntity' fields that reference the entity and include a stable identifier (e.g., Wikidata URI or internal canonical URL).
3. Create or enhance entity hub pages
   • One canonical hub page per entity consolidates facts, schema, canonical links, and a 'sameAs' list (Wikipedia, Wikidata, LinkedIn, YouTube channel).
4. Linking & site graph
   • Internal links should always point to the entity hub; use descriptive anchor text and avoid ambiguous redirects.
5. Structured data types
   • Use schema.org types that match the content: TechArticle, FAQPage, HowTo, QAPage, Dataset, SoftwareSourceCode.
   • Include author, publisher, datePublished, dateModified, and license properties.

Track B — Provenance & Attribution Audit (prove ownership, consent, and license)

Goal: Attach machine-readable provenance to every piece of content and make it easy for platforms to determine attribution and licensing status.

1. Embed provenance metadata in JSON-LD

   Use schema.org properties plus an additive provenance context (W3C PROV) to publish who created, who published, and the license. Example JSON-LD (adapt and automate):

   <script type='application/ld+json'>
   {
     "@context": [
       "https://schema.org",
       "http://www.w3.org/ns/prov#"
     ],
     "@type": "TechArticle",
     "headline": "How to Configure Widget X",
     "author": {"@type": "Person", "name": "Jane Doe", "url": "https://example.com/authors/jane-doe"},
     "publisher": {"@type": "Organization", "name": "Example Corp", "url": "https://example.com"},
     "datePublished": "2025-11-01T12:00:00Z",
     "license": "https://creativecommons.org/licenses/by/4.0/",
     "mainEntity": {"@id": "https://www.wikidata.org/wiki/Q123456"},
     "prov:wasAttributedTo": {"@type": "prov:Agent", "prov:label": "Example Corp"}
   }
   </script>

   This provides a clear machine-readable claim of authorship and license; automate via your CMS template or plugin.

2. Expose a well-known provenance file

   Publish a site-level manifest at /.well-known/content-provenance.json or /.well-known/provenance.json that summarizes licensing policies (training consent: yes/no), contact for licensing, and reference datasets. Example fields:

   • training_consent: true/false
   • default_license: URL
   • contact_for_licensing: email or URL
3. Signal snippet & training control
   • Use data-nosnippet on sensitive blocks to prevent snippet use by some crawlers.
   • Add meta robots directives where you need to restrict excerpts: <meta name='robots' content='noindex, noarchive'> or use X-Robots-Tag headers for non-HTML assets (PDFs, etc.).
   • Consider adding a machine-readable header or meta like <meta name='ai-training' content='no-consent'> and publish the semantics in your /.well-known file; while not yet universally honored, this speeds adoption of standards and provides legal clarity.
4. Register content in licensing marketplaces

   For high-value content, register in content/data marketplaces (post-2025 services) so platforms can license correctly. This creates a contract-level provenance record that AI providers increasingly respect — many publishers are already exploring marketplace and micro-license models in micro-subscriptions and live-drop ecosystems.

Step-by-step audit checklist (practical, runnable)

1. Crawl and inventory

• Run wp-cli or export your sitemap.xml.
• Use Screaming Frog / Sitebulb to capture titles, meta, canonical, schema, and response codes.
• Export to CSV to map pages to entity IDs and licensing flags.

2. Entity mapping

1. For each page, add a column: primary_entity (Wikidata QID or internal URL).
2. Flag pages without a clear entity for editorial review.

3. Structured data audit

• Validate JSON-LD with the Rich Results Test and Schema Markup Validator.
• Ensure author, publisher, datePublished, license, and mainEntity are present where applicable.

4. Provenance & license audit

• Confirm every page shows a license and that the license is machine-readable (JSON-LD + human-readable).
• Publish /.well-known/content-provenance.json with site policy.

5. Technical signals & headers

• Check Canonical, Hreflang, and Link headers for consistency.
• Use X-Robots-Tag for non-HTML assets and embed data-nosnippet for blocks you don't want copied into snippets.

6. Monitoring & detection

• Use GSC & Bing Webmaster to track impressions and CTR for pages appearing in SERP features.
• Set up alerts with your SIEM or logging stack to flag unusual direct content reuse (sudden drop in traffic + no referral sources).
• Periodically query major AI answer surfaces manually (Bing Chat, Google AI Overview) for representative queries tied to your entities.

Practical code & sample commands

Find pages missing license in a WordPress export (example using jq)

# Export site posts to wp export XML, convert to JSON, then:
jq -r '.posts[] | select(.license==null) | .url' site-export.json

Add a single-line JSON-LD license snippet in your template

<script type='application/ld+json'>
{ "@context":"https://schema.org", "@type":"TechArticle", "license":"https://creativecommons.org/licenses/by/4.0/" }
</script>

How to prove attribution when an AI answer omits it

1. Collect the evidence
   • Screenshot the AI answer and record the query that produced it.
   • Capture timestamps and the assistant’s identifier (e.g., Bing Chat session ID) if available.
2. Check the provenance chain
   • See if the assistant provided a source link. If not, check whether your published JSON-LD would have matched.
3. Contact the provider
   • Use the provider’s attribution complaint or licensing contact. Many platforms honor takedown or licensing requests when provenance is machine-readable.
4. Escalate to licensing marketplaces
   • If the content is in a marketplace, use your contract to negotiate attribution or compensation.

Advanced strategies (2026-forward)

Signal entity authority beyond schema

• Publish canonical datasets about your product or topic as open datasets (schema:Dataset) and register them with registries and Wikidata citations.
• Use persistent identifiers (DOI-like or ARK) for high-value content to strengthen provenance.

Machine-readable training consent and licensing APIs

Implement a small API endpoint (e.g., /ai-licensing) that returns JSON outlining consent and licensing details for a URL. This speeds automated licensing checks and is an emerging best practice in 2026.

Entity reputation signals outside your site

• Control your entity’s 'sameAs' network: keep YouTube, GitHub, Wikipedia, and Wikidata entries updated.
• Use digital PR to create authoritative citations on third-party sites that feed knowledge graphs.

Metrics to track (KPIs)

• AI Attribution Rate: percent of AI answers that correctly cite your site when your content is used.
• Entity Visibility: impressions of your entity hub and related queries in GSC/Bing.
• Provenance Coverage: percent of pages with valid JSON-LD provenance + /.well-known entry.
• Monetized Licenses: number of paid licenses or marketplace registrations.

Common pitfalls and how to avoid them

• Assuming meta tags alone prevent training — many crawlers ignore non-standard tags. Use layered signals: JSON-LD, headers, and marketplace contracts.
• Relying solely on canonical tags for entity mapping — canonical is indexing-focused; use explicit 'mainEntity' and 'about' for semantics.
• Not automating provenance — manual metadata will lag. Integrate JSON-LD generation into your CMS publishing workflow.

Case study snapshot (real-world style example)

Scenario: A SaaS docs site noticed a drop in clicks but stable impressions after being cited in AI summaries without attribution. Audit findings:

• Many how-to pages lacked 'author' and 'license' JSON-LD.
• The site had no /.well-known provenance manifest.
• Internal linking was inconsistent, so AI systems couldn’t reliably identify the brand hub.

Fixes implemented:

• Automated JSON-LD injection in templates, including license and mainEntity.
• Published /.well-known/content-provenance.json and registered core docs in a licensing marketplace.
• Built an entity hub page with Wikidata links and consolidated internal linking.

Outcome (90 days): Attribution rate improved, click-through from AI answers recovered by 38%, and the org secured two small licensing deals for training data.

Future predictions (2026–2028)

• Major AI platforms will standardize a small set of provenance signals (JSON-LD + /.well-known + licensing API).
• Marketplaces and exchanges for content licensing will become routine for publishers; proof-of-origin registers (blockchain or registrar models) will be used for high-value content.
• Search will continue to blend traditional ranking with entity-first AI answers; consistent entity signals across social and your documentation site will be the single biggest differential for visibility.

Quick checklist (printable)

• Map pages to entities (Wikidata or internal IDs)
• Add JSON-LD with author, publisher, license, mainEntity
• Publish /.well-known/content-provenance.json
• Use data-nosnippet or X-Robots-Tag where needed
• Register critical content in licensing marketplaces
• Monitor AI-answer surfaces and log evidence of reuse

Closing: Start the audit in one hour

You can begin a targeted audit in under an hour: export a sitemap, sample 20 high-value pages, and confirm whether each has JSON-LD with author/publisher/license/mainEntity. If not, add a temporary JSON-LD snippet and publish a /.well-known provenance file.

Final thought: In 2026, visibility in AI answers is as much about being a trusted entity as it is about on-page SEO. Treat provenance as part of your product: it protects rights, unlocks licensing revenue, and improves AI attribution — which together keep your traffic, brand, and revenue where they belong.

Call to action

Ready to run a provenance + entity SEO audit? Download our free 30-point audit checklist, or book a 30-minute consult and we’ll walk your tech and editorial teams through the first automated JSON-LD rollout. Click to get the checklist and schedule time with a documentation auditor.

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