Here’s why it doesn’t work in practice
In a major tactical pivot in late 2025, the EU Council officially dropped the phrase mandatory detection orders for encrypted services. On paper, this looks like a victory for privacy. However, the 2026 reality is more nuanced, and perhaps more legally complex as we are approaching the April 3, 2026 deadline for the temporary rules, are we on the way to mass-surveillance?
The “Voluntary” Paradox
The new framework replaces the order to scan with an obligation to manage risk. Under the proposed Chat Control 2.0, services are categorized as High, Medium, or Low risk.
The Trap: If a service (like WhatsApp or Signal) is deemed “High Risk” for abuse, it must implement “all reasonable mitigation measures.”
The “Nudge”: While scanning isn’t “mandatory,” it is listed as a primary tool for mitigation. A service that refuses to scan and then experiences a safety incident could face astronomical fines for “failing to mitigate risk.”
Privacy Perspective: This is “privatized” surveillance. The state isn’t breaking your door down; it’s telling the landlord they’re liable if they don’t install a camera in your bedroom.
Client-Side Scanning: The Undead Technology
Even though the Council claims to protect encryption, they still advocate for scanning “prior to transmission.” This is the technical definition of Client-Side Scanning (CSS). Mathematically and logically, CSS creates a state-mandated vulnerability. If an app scans your photo to compare it against a database before it encrypts it, the “end-to-end” promise is broken at the very first “end”—your phone.
The Age Verification Pivot
As the battle over scanning reached a stalemate, 2026 has seen a surge in Mandatory Age Verification.
The Goal: To “gate” private messaging, requiring users to prove their identity via government ID or biometric “face-age” estimation.
The Privacy Cost: This ends the era of the anonymous digital whisper. To speak privately, you must first tell the platform exactly who you are, creating a massive honeypot of identity data ripe for hacking.
Analysis: Does it protect encryption?
| Feature | Official Claim | Privacy Reality |
| End-to-End Encryption | “Remains untouched and protected.” | Undermined by “pre-encryption” scanning requirements. |
| Detection Orders | “Dropped for encrypted services.” | Replaced by “Voluntary” scanning pressured by high legal liability. |
| User Safety | “Targeted only at illegal material.” | Relies on error-prone AI that flags legal, private content (The “Mark” Case Study). |
The Verdict: A Chilling Spring toward Mass-surveillance?
As we approach the April 3, 2026 deadline for the temporary rules, the EU is attempting a delicate “legal magic trick.” By renaming surveillance as “risk mitigation,” they hope to bypass the fierce opposition of the European Parliament. For the average user, the walls are still turning to glass; the only difference is who is being asked to hold the Windex. To understand why the Glass Corridor is so technically contentious, we have to look at the two distinct engines driving the scanning: Hash-Matching and AI Classifiers. While they are often grouped together in policy debates, they represent two very different levels of intrusion and reliability.
Technical Breakdown: The Two Faces of Detection
Hash-Matching: The “Digital Mugshot”
Hash-matching is the more established technology. It relies on a database of “perceptual hashes”—unique digital fingerprints of images that have already been identified as illegal.
How it works: When you send a photo, the algorithm creates a “fuzzy” fingerprint of it. It doesn’t “see” the image like a human; it sees a mathematical signature. It then checks this signature against a list of known “bad” signatures.
The Pros: It is relatively fast and has a lower false-positive rate because it is looking for something that has already been verified by a human expert.
The Cons: It is blind to anything new. If a criminal creates a new image today, hash-matching won’t catch it until that image is manually added to the database.
AI Classifiers: The “Predictive Detective”
This is where the 2026 “Chat Control 2.0” debate gets truly heated. AI classifiers don’t look for specific files; they look for patterns and likelihoods.
How it works: A machine-learning model is trained on thousands of illegal images to “learn” what abuse looks like. When it scans your private messages, it asks: “What is the probability that this new, never-before-seen image contains illegal content?”
The Pros: It can catch “novel” material—the first-generation images that hash-matching misses.
The Cons: It is prone to “hallucinations” and false positives. AI lacks context. It might flag a breastfeeding photo, a beach picture, or a medical image (like in the “Mark” case study) simply because the skin tones or composition match its training data.
The Accuracy Gap (2026 Projections)
The following table highlights the “Success vs. Suspicion” ratio that has privacy advocates worried.
| Feature | Hash-Matching (Known) | AI Classifiers (Novel/Unknown) |
| Detection Method | Signature Comparison | Pattern Recognition |
| Accuracy Rate | ~99.9% (on exact matches) | ~88% – 95% (estimated) |
| Privacy Intrusion | High (Scans all content) | Critical (Attempts to “understand” content) |
| False Positives | Rare but possible | High (The “Context Blindness” problem) |
| Goal | Preventing re-distribution | Identifying new victims |
The “Mass Denunciation” Math
In a 2026 simulation by digital rights researchers, applying a 95% accurate AI classifier to the EU’s daily messaging volume resulted in roughly 22 million false reports per year.
For law enforcement, this creates a “Data Haystack” problem: they are so flooded with reports of innocent families that the actual criminals, who are increasingly using “adversarial” techniques to trick the AI, become harder to find.
Privacy Note: Using AI classifiers on end-to-end encrypted apps is the technical equivalent of having a police officer stand behind you to read your mail before you seal the envelope, occasionally shouting “Suspect!” because your handwriting looks like someone else’s.
The Final Shift: Why it Matters Now
As the trilogue negotiations move toward the June 2026 final deal, the focus has shifted to “Targeted Detection Orders.” Proponents claim this solves the privacy issue by only scanning “suspected” groups. However, critics point out that the criteria for “suspicion” are often broad enough to cover entire regions or demographic groups, effectively keeping the Glass Corridor intact.
Written by LarsGoran Bostrom
Expert of Data Ethics and Developer/Author of the Course: Data Ethics – Navigating the Ethical Landscape of Emerging Technologies
Also read my Trend Report on this subject on Europeantrends.net