Audit Checklist: Detecting AI‑Generated Content on Your Site

Introduction — Why an AI content audit matters

As AI writing tools become pervasive, site owners must be able to detect, verify, and manage AI‑generated content to protect quality, E‑E‑A‑T, and search visibility. This checklist gives SEO and content teams a practical audit workflow, the common signals of machine‑produced content, and the testing tools and evidence you should collect during an investigation.

Use this article as an operational playbook: sample checks you can run immediately, a prioritized audit process, and remediation steps for different risk levels (low, medium, high).

• Scope: pages, blog posts, product descriptions, syndicated content, user‑generated content.
• Goal: identify likely AI‑generated content, confirm through layered tests, and take proportionate remediation.

Common signals & red flags (what to look for)

Detecting AI‑generated content is rarely a single definitive test. Look for clusters of signals across content, metadata, behavior, and technical traces.

Content-level signals

• Generic phrasing: repetitive patterns, templated intros/outros, over‑use of transition phrases.
• Surface accuracy: plausible but unverifiable claims, missing specific examples, few unique insights.
• Inconsistent voice/tone: sections that shift style, register, or depth without clear author reasoning.
• Sentence structure oddities: unusual punctuation, oddly formal phrasing, or sudden simplicity in complex topics.

Metadata & author signals

• Missing or generic author bios, new authors with many simultaneous posts, or identical bylines across unrelated topics.
• CMS creation timestamps inconsistent with edit history (e.g., many posts created in a short time window).

Behavioral & engagement signals

• Low time on page with normal traffic, unusually high bounce on content-heavy pages, or lack of organic link growth for perceived-high-value pages.

Technical & image signals

• Images with reversed or traced stock images (use reverse image search), missing camera EXIF where expected, or image filenames that look auto‑generated.

Severity grows when multiple signals point to machine generation. Use this signals checklist to prioritize manual review.

Tools & test methods — layered detection approach

No single detector is definitive. Combine automated tools, forensic checks, and manual review for reliable results.

Automated detectors (first pass)

• Run multiple detectors to reduce bias: statistical classifiers, perplexity/entropy measures, and published detectors. Record results and confidence scores.
• Note: automated tools give probabilistic signals and can produce false positives, especially on short text or highly edited AI outputs.

Stylometry & linguistic analysis

• Analyze sentence length distribution, vocabulary richness, and unique n‑gram patterns. Compare against known author samples (if available).

Metadata, CMS & server checks

• Inspect CMS revision history, IPs of contributors, user accounts, timestamps, and bulk upload patterns.
• Check file creation vs. publication times, author account creation date, and editorial workflow records.

Verification & provenance tests

• Search verbatim excerpts (or paraphrase strings) to detect syndication or cross‑site copying.
• Use reverse image search for images, and check EXIF for original creation details where applicable.

Manual sampling

• Have subject matter experts review a stratified sample: high‑traffic pages, flagged pages, recent bulk uploads.
• Record qualitative judgments (unique insight present, factual depth, citation quality).

Keep evidence: screenshots, detector outputs, CMS logs, and reviewer notes — store these with timestamps for audits and appeal processes.

Audit process, checklist & remediation workflow

This is a prioritized, repeatable audit flow you can adopt.

Step‑by‑step audit process

1. Define scope & thresholds: target segments (e.g., new posts last 30 days, product descriptions) and set risk thresholds for automated flags.
2. Run automated scans: batch detectors across the scoped content, export results, and rank by risk score.
3. Sample & triage: stratify by risk, traffic, and business impact; prioritize high‑impact pages for manual review.
4. manual review: subject matter expert checks for factual depth, unique analysis, and citation quality; record verdicts and evidence.
5. Report & decide: assemble findings into an audit report with recommended actions and confidence levels.
6. Remediate & monitor: remediation actions below — then monitor KPIs and re‑scan on a schedule.

Practical remediation actions by severity

• Low risk: add author attribution, citations, and minor edits to add original analysis.
• Medium risk: substantially rewrite with SME input, add unique research, or temporarily de‑index until verified.
• High risk: remove or de‑publish content, investigate content source (vendor, contractor), and take account action if abuse is confirmed.

Checklist (quick reference)

• Have automated detectors been run and results exported?
• Is there CMS evidence (revisions, author history) for each flagged page?
• Was a human SME review completed for prioritized items?
• Are screenshots and logs stored in a central audit folder?
• Is remediation logged with date, action, and owner?
• Has monitoring been scheduled (weekly/monthly) to catch regressions?

Final recommendations: adopt a mixed approach (automated + human), document every decision, and embed AI‑usage policy into editorial guidelines. Regularly retrain your sampling strategy as AI outputs evolve and maintain a lightweight appeals process for disputed removals.

Next steps: run a pilot audit on your top 100 pages (by traffic and revenue) using this checklist, then expand to sitewide sampling once workflow and tooling are validated.