Cookie Compliance in 2026: Why Consent Banners Don’t Prevent Enforcement Actions

Having a consent banner deployed does not mean your cookie practices are compliant. While regulators still routinely penalize broken banner interfaces, enforcement actions and class-action lawsuits have increasingly shifted to what happens after the banner: the actual data flows between your site, third-party scripts, and external servers.

• Fixing your CMP configuration is step one, but it is not the finish line. Regulators still levy massive fines for missing “Reject All” buttons or dropping cookies before consent, but these are just the low-hanging fruit.
• Consent banners do not inherently control data flows. Most complex enforcement actions target back-end data flow failures where scripts ignore opt-outs or bypass Global Privacy Control (GPC) signals. First-party publishers are bearing the brunt of this liability.
• Cookie risk is categorized by Purpose and Data Exchange. The outdated “Essential/Analytics/Marketing” model is dead. Modern compliance requires evaluating trackers through the lens of EU IAB TCF purposes and US State Law definitions of “Sale” and “Share.”
• “Cookieless” tracking is not a compliance loophole. Regulators are aggressively targeting device fingerprinting and cookie syncing, ruling that “shadow tracking” techniques that bypass traditional cookies are still strictly subject to consent and opt-out requirements.

For years, regulatory sweeps focused heavily on UI/UX, penalizing companies for dark patterns, missing “Reject All” buttons, or confusing category toggles. This is still the easiest way to get fined because it requires zero technical investigation for a regulator, NGO, or private litigator to spot a broken interface using automated sweeping tools. For example, the European privacy NGO NOYB launched a massive automated campaign lodging 226 complaints against deceptive cookie banners in a single sweep, illustrating just how heavily automated these legal challenges have become. Similar sweeps were organized by the UK’s ICO and the CalPrivacy for GPC failures.

This wave of enforcement began with the French CNIL’s landmark January 2022 fines against Google (€150 million) and Facebook (€60 million) for making it harder to reject cookies than to accept them. It continues today with massive penalties, such as the €10 million fine against Yahoo in late 2023 for failing to respect cookie choices, and the €150 million CNIL fine against SHEIN in late 2025 for placing up to 10 different types of cookies immediately upon a user’s arrival (before any interaction with the banner).

Fixing your Consent Management Platform (CMP) configuration takes a week and prevents these obvious fines. But a perfect banner is only the beginning.

A CMP presents choices and records preferences, but it rarely has the technical authority to stop a deeply embedded marketing pixel from misfiring.

When a marketing team adds a tracking pixel to a site, that script executes in the first-party context. This gives it access to session identifiers, login states, location data, and other user-specific DOM values. A third-party script running in this context can read those values and transmit them to its own servers without the organization knowing. (Even nested elements pose risks: see our guide on whether iframes can place cookies on your site without user consent).

Recent enforcement under US state laws proves that publishers are strictly liable for the disconnect between their user-facing forms and their third-party ecosystem, especially when it comes to honoring automated signals like Global Privacy Control (GPC). 

• The Walt Disney Company ($2.75 million CCPA settlement, Feb 2026): In a record-breaking sweep, the California Attorney General fined Disney because its opt-out mechanisms were fragmented. If a logged-in user transmitted a GPC signal, Disney only honored it on that specific device. Furthermore, Disney’s web forms stopped internal data sharing, but failed to block embedded third-party ad-tech vendors from extracting data.
• Healthline Media ($1.55 million CCPA settlement, July 2025): The popular health publisher had a consent mechanism, but a misconfiguration meant it failed to honor consumer opt-outs (including GPC) for targeted advertising. Healthline inadvertently shared highly sensitive article titles with ad-tech vendors, triggering strict purpose limitation violations.
• Tractor Supply ($1.35 million CPPA fine, Sept 2025): The California Privacy Protection Agency hit the retailer with a massive administrative fine for failing to configure its site to recognize and honor GPC signals, rendering browser-based opt-outs completely ineffective.

In all these cases, the violation was not the absence of a privacy policy or a banner. It was the publisher’s failure to control the actual network traffic routing to third parties.

Before categorizing your trackers, you must understand how they operate. Every third-party interaction involves one of three observable network actions:

1. Storage (cookie creation) triggers the EU opt-in requirement. If a cookie drops before interaction (as seen in the SHEIN case), the violation occurs instantly
2. Transmission (cookie values sent in an outbound request) carries the US “Sale/Share” risk. When your site sends an identifier to a programmatic bidstream, you are legally responsible for that transfer.
3. Observation (scripts reading DOM values). This is where traditional cookie law bleeds into massive data breaches. The Form-Scraping Pixel Risk occurs when third-party scripts (like Meta Pixel or TikTok Pixel) read form fields, shopping carts, or URLs via JavaScript and exfiltrate the data. Recent massive fines against BetterHelp ($7.8M, FTC), Cerebral ($7M, FTC), and two Swedish Pharmacies (€3.9M, IMY) were all caused by “Observation” failures where pixels silently scraped sensitive health data from the page.

The decade-old ICC UK categories (Essential, Analytics, Marketing) are vastly underpowered for 2026. Effective cookie governance requires evaluating trackers through two modern regulatory lenses:

Lens 1: The EU/ePrivacy Approach (IAB TCF v2.2)

Under the GDPR and ePrivacy Directive, risk is tied to highly granular purposes and features. The IAB TCF defines 10 distinct purposes, 2 special purposes, and 5 features/special features (see the Appendix for a demystified breakdown).

This sheer complexity is intentional: it reflects the massive, fragmented nature of the ad-tech ecosystem. A CPO can no longer just search Cookiepedia and copy the “marketing” label. You must know if a vendor is using Purpose 1 strictly to store an identifier, or if they are combining it with Purpose 3 (Personalised Ads Profiling) and Feature 2 (Cross-Device Linking) to feed programmatic bidstreams. Manually auditing and mapping these permutations across dozens of evolving third-party scripts to understand what a cookie actually does is virtually impossible without automation.

Lens 2: The US State Law Approach (Sale, Share, Targeted Ads)

Unlike the EU’s opt-in model, the US operates on an opt-out basis driven by the exchange of value. 

• Targeted Advertising (Cross-Context): Tracking a user across non-affiliated sites to predict preferences. Under California law (and other states following the strong model), this is explicitly regulated as a “Share” and must be blocked upon a GPC signal.
• Sale (Valuable or Monetary Exchange): A “Sale” occurs even if no cash changes hands. If an analytics vendor (like Google or LiveRamp) collects data from your site to improve their own machine-learning models or benchmark reports, it constitutes a Sale in states like California, Colorado, and Texas.
• Exemptions (Service Providers): The only safe harbor. Data passed strictly to provide the requested service is exempt, provided strict Data Processing Agreements explicitly prohibit the vendor from using the data for their own purposes.

Even organizations with mature CMPs can be exposed by tracking techniques explicitly designed to bypass conventional controls.

How Does Cookie Syncing Work?

Cookie syncing is the mechanism through which separate ad-tech vendors link their user identifiers to build unified profiles across domains. When your site loads a vendor’s pixel, it triggers a background redirect chain, passing that vendor’s identifier to other partners and creating a match table linking their profiles.

For example, in the live ID syncing chain captured by Vault JS below, you can see a single unique identifier (123b5ebe-d462-4110-b2df-9e043b5d2faa) minted by The Trade Desk (match.adsrvr.org). Within milliseconds, that exact token is extracted and shared across 7 other ad-tech platforms, including Google DoubleClick, Adobe, Criteo, Magnite, Microsoft/Xandr, Index Exchange, and PubMatic.

If your CMP fails to block the initial syncing request, you are facilitating a hidden profile update across the entire ecosystem. Regulators have explicitly targeted this: in 2023, the French CNIL fined Criteo €40 million, highlighting that Criteo’s massive identity-syncing technology failed to verify whether the partners supplying the synced data had actually collected valid consent.

As third-party cookies face browser deprecation, ad-tech relies increasingly on device fingerprinting. (See our introductory guide: What is Device Fingerprinting and Why Cannot Your Consent Tool Stop It?).

If a company thinks they are compliant simply because they “do not use cookies,” they are deeply mistaken. In a landmark enforcement action against the airline Vueling, the Spanish AEPD ruled that Article 22.2 of the LSSI (the Spanish ePrivacy Law) applies to any storage and retrieval of data on a device. The AEPD doubled down on this in its 2024 “Cookieless Monsters” technical guidance, explicitly stating that Canvas, Font, and Audio fingerprinting create highly precise profiles and are strictly illegal for marketing without explicit opt-in consent. (For advanced evasion tactics, see Server-Side Fingerprinting Explained).

CNAME cloaking is a DNS-level technique where a site operator points a subdomain (e.g., tracking.example.com) to a third party’s infrastructure. The browser treats cookies from that subdomain as first-party, bypassing protections like Safari’s ITP. The CNIL has explicitly addressed this, confirming that consent requirements still apply, and warning of massive security risks since the cloaked subdomain can access all secure session tokens.

Enforcement has shifted from billion-dollar penalties against global platforms toward targeted actions against first-party publishers who lose control of their third-party ecosystem.

Organizations that rely on static banner configurations and periodic manual audits are operating with an incomplete picture. Cookie syncing operates through invisible redirect chains. CNAME cloaking disguises tracking at the DNS level. “Cookieless” fingerprinting evades basic cookie blockers. In 2026, the question is not “Is your banner turned on?” but rather, “Do you know what your scripts are actually doing when a user opts out?” Compliance is no longer just a UI checkbox; it is a network traffic reality.

To solve this, Vault JS’s new AI-Powered Compliance Engine uses Generative AI to automatically predict and map every tracker on your website to complex IAB Purposes and fragmented US State Privacy Laws. Instead of manually auditing scripts, Vault JS provides both a deep technical analysis of what each script is actually doing (e.g., “Meta Pixel is extracting email hashes”) alongside auto-generated, user-friendly explanations formatted perfectly for your CMP.

The compliance teams best positioned to manage this risk are those with continuous, outside-in visibility into what their ecosystem is doing today. Vault JS monitors cookie behavior, pixel data flows, and third-party script activity across your digital properties without installing code on your site. Request a free site analysis to see what your third-party ecosystem is actually doing behind your consent banner today.

The IAB Transparency and Consent Framework (TCF) is the standard for European ad-tech compliance. However, with 10 Purposes, 2 Special Purposes, 3 Features, and 2 Special Features, compliance teams routinely struggle with overlap and confusing legal jargon.

Here is a simplified breakdown of the core IAB Purposes:

Storage vs. Processing 

• Purpose 1 (Store and/or access information on a device): This is purely the ePrivacy requirement (Cookie Law). It covers the technical act of reading or writing an identifier to a device. It is not a data processing purpose in itself (GDPR)—any data read via Purpose 1 must have an additional purpose (like Purpose 3) to actually be used.

Advertising Purposes

• Purpose 2 (Select basic ads): Delivering contextual ads based on real-time data (like the page URL or non-precise location). No profiling allowed.
• Purpose 3 (Create a personalised ads profile): The highest-risk activity. Tracking a user across sites to build an interest profile.
• Purpose 4 (Select personalised ads): Actually serving an ad to a user based on the profile created in Purpose 3.

Content Purposes

• Purpose 5 (Create a personalised content profile) & Purpose 6 (Select personalised content): Identical mechanics to Purposes 3 & 4, but strictly for non-advertising content (e.g., article recommendations).

Measurement & Research

• Purpose 7 (Measure ad performance): Checking if an ad was viewed, clicked, or resulted in a sale.
• Purpose 8 (Measure content performance): Analytics to see how users interact with the site’s organic content.
• Purpose 9 (Apply market research): Using measurement data to generate aggregate audience insights (often mapped against offline panel data).
• Purpose 10 (Develop and improve products): Using data specifically for machine learning and R&D to build new vendor products.

The “No Opt-Out” Special Purposes

• Special Purpose 1 (Ensure security, prevent fraud, and debug) & Special Purpose 2 (Technically deliver ads or content): These are strictly necessary for the internet to function. Users cannot opt out of these via the TCF.

Karel Kubicek
Senior Privacy Researcher, Vault JS

He holds a PhD from ETH Zurich in automated privacy compliance and was previously a postdoctoral researcher at INRIA. His work focuses on using machine learning to measure and detect privacy violations at scale, and he led the development of CookieBlock, a privacy-enhancing browser extension with over 20,000 installations that received a USENIX Security Distinguished Artifact Award.