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The Illusion of Privacy in Modern Messaging Apps
For years, messaging applications have marketed themselves as secure digital sanctuaries where users can freely exchange personal conversations, sensitive information, business discussions, family memories, and even confidential documents. Millions of people rely on these platforms every day, believing that end-to-end encryption alone guarantees complete privacy.
A new cybersecurity study paints a far more complicated picture.
According to research conducted by cybersecurity company Surfshark, the messaging landscape of 2025 has reached a critical turning point. While encryption has become more widespread than ever, another technology is rapidly expanding inside messaging platforms: artificial intelligence. The study found that 90% of the most popular messaging apps now integrate AI-powered features that may introduce new privacy concerns, potentially exposing user information in ways many people do not fully understand.
The findings reveal a growing contradiction in the technology industry. Companies continue strengthening encryption technologies while simultaneously deploying AI systems that often require access to user conversations, metadata, behavioral patterns, and other forms of personal information. As a result, users may feel protected by encryption while unknowingly contributing valuable data to increasingly sophisticated AI ecosystems.
The question is no longer whether your messages are encrypted. The real question is how much information surrounding those messages is still being collected, analyzed, stored, and monetized.
Surfshark Investigates the Most Popular Messaging Platforms
Surfshark’s research focused on the ten most popular messaging applications available on Apple’s iOS platform during 2025. Rather than simply reviewing marketing claims, researchers analyzed actual privacy disclosures, encryption standards, AI implementations, and data collection practices.
The study evaluated 35 different categories of user information disclosed through Apple’s App Store privacy labels. These categories included everything from contact information and location data to browsing behavior, device identifiers, purchase history, and user-generated content.
The goal was simple: determine which messaging platforms genuinely prioritize user privacy and which ones aggressively collect information beyond what is necessary for communication.
What researchers discovered highlights a widening divide between privacy-focused platforms and data-driven technology ecosystems.
Encryption Has Become the New Industry Standard
One positive finding emerged from the report.
Nine out of the ten messaging applications studied now provide end-to-end encryption for at least part of their communication systems. This represents a major shift compared to previous years when encryption remained limited to only a handful of privacy-focused services.
End-to-end encryption ensures that messages remain unreadable to anyone except the intended sender and recipient. Even the platform provider should theoretically be unable to access message contents during transmission.
This advancement significantly improves protection against cybercriminals, government surveillance efforts, and unauthorized interception attempts.
Combined with a trusted VPN service that encrypts broader internet traffic and masks IP addresses, users can achieve a strong baseline level of communication security.
Yet encryption alone does not eliminate all privacy risks.
Why Encryption Does Not Tell the Whole Story
Many users mistakenly assume that encrypted messages automatically mean complete privacy.
In reality, encryption primarily protects message content. It does not necessarily prevent companies from collecting surrounding information such as:
Device identifiers
Contact lists
Usage habits
Behavioral patterns
Location information
Advertising profiles
Metadata regarding communications
Metadata can often reveal remarkably detailed information about an individual’s life even without exposing actual message contents.
Who you talk to, how frequently you communicate, where you communicate from, and what devices you use can collectively create an extensive digital profile.
For technology companies dependent on advertising revenue, this information carries enormous commercial value.
Signal Emerges as the Undisputed Privacy Champion
Among all applications analyzed, Signal achieved the highest privacy score by a significant margin.
Signal received an exceptional score of 0.99, making it the strongest privacy-focused messaging platform in the study.
Its approach is remarkably simple.
The platform collects only a phone number and avoids invasive user tracking mechanisms commonly found elsewhere in the industry.
Signal has built its reputation around minimizing data collection from the beginning. Rather than gathering information first and protecting it later, the service follows a philosophy of collecting as little user information as possible.
Even if someone attempted to obtain user data from Signal, there would be very little available to retrieve.
This principle represents one of the strongest privacy protections available in modern digital communication.
Quantum-Secure Protection Sets Signal Apart
Another important distinction identified by researchers involves quantum-resistant cryptography.
Signal and
While practical quantum attacks remain largely theoretical today, cybersecurity experts increasingly warn that future quantum computers could eventually break many current encryption standards.
Quantum-secure cryptography aims to prepare for that future before it becomes a crisis.
For privacy-conscious users, this additional layer of protection demonstrates long-term security planning rarely seen across mainstream communication platforms.
Meta Messenger and LINE Rank Among the Worst Offenders
At the opposite end of the privacy spectrum sit Meta Messenger and LINE.
The report identified these platforms as some of the most aggressive collectors of user information.
Messenger, in particular, stood out for collecting 32 of the 35 possible data categories analyzed.
That figure is nearly double the industry average.
Even more concerning, researchers noted that 30 of those collected categories may be used for purposes unrelated to the application’s core communication functions. These purposes often include targeted advertising, marketing optimization, behavioral profiling, and personalized product recommendations.
Such extensive data collection creates massive datasets capable of revealing detailed insights into user behavior and preferences.
For privacy advocates, this level of information gathering raises serious questions regarding necessity, transparency, and user consent.
Discord’s Encryption Problem
Discord received special attention for a different reason.
Unlike most major messaging platforms included in the study, Discord was identified as the only service that does not provide end-to-end encryption for standard text communications.
While Discord remains extremely popular among gaming communities, businesses, developers, and online communities, the lack of comprehensive encryption places it behind competitors from a privacy standpoint.
Researchers also noted that Discord, along with LINE and Rakuten Viber Messenger, actively collects data specifically for tracking purposes.
This distinction separates these platforms from services whose data collection focuses primarily on operational functionality.
Artificial Intelligence Is Creating New Privacy Challenges
Perhaps the most significant finding from
An overwhelming 90% of analyzed messaging applications now incorporate AI-powered functionality.
Features include:
Automated message summaries
Smart replies
Language translation
Chat assistants
AI-generated content suggestions
Conversational bots
On the surface, these capabilities offer convenience and productivity benefits.
The hidden tradeoff lies in data access.
For AI systems to summarize conversations, generate responses, or perform contextual analysis, they often require visibility into communication content or associated metadata.
This introduces an entirely new category of privacy considerations.
Even if encryption protects messages during transmission, AI processing may require portions of that information to be analyzed within company-controlled environments.
The result is a growing tension between convenience and confidentiality.
Academic Researchers Sound the Alarm
Experts from both New York University and Cornell University contributed warnings referenced within the Surfshark findings.
According to researchers, AI technologies are advancing faster than many existing security frameworks can adequately address.
The concern is not merely theoretical.
Every new AI feature potentially expands the attack surface available to malicious actors. Poorly secured AI systems, flawed implementation practices, or insufficient transparency regarding data usage could introduce vulnerabilities into platforms that were previously considered highly secure.
The speed of AI deployment across the technology industry has intensified these concerns.
Companies are racing to release AI features, often under intense competitive pressure.
Security reviews sometimes struggle to keep pace with that rapid innovation cycle.
Human Error Remains the Greatest Security Weakness
While encryption, privacy controls, and AI safeguards receive significant attention, cybersecurity experts consistently identify one vulnerability that remains impossible to eliminate completely: human behavior.
Recent warnings from both the FBI and the Cybersecurity and Infrastructure Security Agency highlighted sophisticated phishing campaigns targeting users of secure messaging platforms, including Signal.
Attackers increasingly focus on manipulating people rather than breaking encryption.
If users willingly provide credentials, approve malicious login requests, or fall victim to social engineering tactics, even the strongest cryptographic protections become ineffective.
Security technologies can reduce risk.
They cannot completely eliminate poor decisions, deception, or human mistakes.
What Undercode Say:
The Surfshark study highlights a critical transformation occurring across the messaging industry. For years, privacy discussions focused primarily on encryption standards. Today, the conversation must expand beyond encryption and examine data ecosystems surrounding communication platforms.
The most important takeaway is that encryption and privacy are no longer interchangeable concepts.
A service may offer world-class encryption while simultaneously collecting extensive behavioral data.
This creates a misleading perception among users who often equate encrypted communication with total anonymity.
Signal’s success demonstrates that privacy-first business models remain technically achievable.
The company proves that secure communication does not require massive behavioral surveillance.
Meanwhile, advertising-driven platforms face an inherent conflict of interest.
Their business model rewards data collection.
More data creates better advertising profiles.
Better advertising profiles generate more revenue.
The arrival of AI intensifies this conflict.
Artificial intelligence thrives on data.
Machine learning systems improve through exposure to larger datasets.
Companies deploying AI assistants therefore possess strong incentives to expand information access.
This trend could gradually erode many privacy protections users currently expect.
Another overlooked aspect involves metadata.
Most public discussions focus on message content.
Metadata frequently reveals more about individuals than actual conversations.
Communication patterns expose relationships.
Timing reveals habits.
Location data exposes routines.
Device identifiers connect activity across services.
Combined together, metadata becomes extraordinarily powerful.
Quantum-resistant encryption is another important development.
Although quantum computing threats remain distant, organizations implementing protections today demonstrate strategic security planning.
Signal and iMessage deserve recognition for moving beyond short-term security considerations.
Discord’s lack of comprehensive end-to-end encryption reflects a growing divide between communication-first and community-first platforms.
Different design priorities produce different security outcomes.
Users must understand these distinctions before selecting communication tools.
Regulators may eventually increase scrutiny of AI-powered messaging features.
Data governance frameworks currently lag behind AI deployment speeds.
Future regulations could require clearer disclosure regarding AI data processing.
Consumers are becoming increasingly privacy-aware.
Platforms collecting excessive information may face reputational risks.
Trust is becoming a competitive advantage.
The companies that minimize collection rather than merely securing collection may gain long-term user loyalty.
Privacy is evolving from a niche concern into a mainstream purchasing decision.
Organizations that ignore this trend risk alienating future users.
The
Users will demand clearer explanations regarding how conversational data is processed.
The distinction between “encrypted” and “private” will become increasingly important.
Cybersecurity strategies must adapt accordingly.
Ultimately, the safest data remains the data that was never collected.
That principle remains as relevant today as it was a decade ago.
Deep Analysis
Evaluating Open Network Connections
ss -tulpn
Monitoring Active Processes
ps aux --sort=-%mem
Checking DNS Requests
sudo tcpdump -i any port 53
Inspecting Established Connections
netstat -antp
Monitoring Network Traffic
sudo iftop
Capturing Suspicious Packets
sudo tshark -i any
Viewing Firewall Rules
sudo iptables -L -n -v
Checking Listening Services
sudo lsof -i -P -n Analyzing SSL/TLS Certificates
openssl s_client -connect signal.org:443
Reviewing System Authentication Logs
sudo grep "Failed password" /var/log/auth.log
Monitoring Real-Time Logs
journalctl -f
Identifying Potential Tracking Domains
sudo tcpdump -i any | grep tracker
Scanning Local Services
nmap localhost
Detecting Outbound Connections
sudo netstat -plant
Auditing Installed Applications
dpkg -l
✅ Surfshark’s research found that the overwhelming majority of major messaging applications now support end-to-end encryption, confirming encryption has become an industry standard.
✅ Signal consistently ranks among the most privacy-focused communication platforms because it minimizes data collection and avoids advertising-based tracking models.
✅ AI-powered messaging features increasingly require access to user data for processing, creating legitimate privacy concerns that cybersecurity researchers and academic institutions continue to study.
❌ End-to-end encryption alone does not guarantee complete privacy. Metadata collection, account tracking, device identifiers, and behavioral analytics can still expose significant information about users.
Prediction
(+1) Privacy-focused messaging platforms such as Signal will continue gaining users as public awareness of data collection practices grows.
(+1) AI transparency requirements will become a major regulatory priority, forcing messaging providers to clearly disclose how user conversations are processed and analyzed.
(+1) Quantum-resistant encryption technologies will expand across the messaging industry as future-proof security becomes a competitive selling point.
(-1) Advertising-driven messaging platforms may face increased criticism and trust issues if aggressive data harvesting practices continue to expand.
(-1) AI assistants integrated into messaging applications could become attractive targets for cybercriminals seeking access to valuable conversational data.
(-1) Users who rely solely on encryption while ignoring phishing threats will remain vulnerable to account compromise despite stronger cryptographic protections.
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References:
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