Americans Pull Back on Subscriptions as Costs Rise and Habits Shift

A survey from June 2025 shows that Americans are paying for fewer subscriptions than before. The research asked 1,138 adults about their monthly habits and spending. On average, households now have 2.8 subscriptions compared with 4.1 in 2024.

Monthly costs dropped as well. People spent about 59 dollars a month last year. This year, the average is 37 dollars. That means many households are saving roughly 264 dollars over a year by cutting services.

Wasted Spending Remains

Even with fewer accounts, people are still paying for things they don’t use. The study found that each person wastes about 127 dollars a year on unused subscriptions. That figure equals nearly three months of typical subscription spending.

Younger adults are more likely to forget about old accounts. Those between 18 and 24 had fewer subscriptions overall, but they wasted more money compared with older groups. People in the 35 to 54 age bracket held the most subscriptions, while older adults over 55 tended to be more careful.

Streaming Under Pressure

Streaming platforms continue to dominate, with 53 percent of households paying for at least one service. Even so, sign-ups are down from earlier years. Price increases and new rules on password sharing have caused many to cancel.

The shift also changes viewing habits. Around 46 percent of people said they would be more likely to turn to piracy if they cancelled their streaming accounts. That number shows how pricing decisions can influence behaviour outside the platforms themselves.

Food Delivery and Shopping Services

Food delivery subscriptions make up a smaller share. About 15 percent of people pay for passes such as Caviar or Grubhub, though many said they hardly use them.

Retail subscriptions also exist, covering items like cosmetics or household goods. About 10 percent of households keep one of these. The study suggests many of these memberships remain active out of habit rather than frequent use.

Fitness and Dating Apps

Fitness and dating services are less common but still part of the picture. Around 5 percent of people pay for fitness programmes, and 4 percent use dating app subscriptions. Both follow the same pattern seen elsewhere: people sign up, lose interest, and forget to cancel.

Why People Cancel Subscriptions

The survey also asked participants what pushed them to cancel. Rising living costs topped the list, with about a third citing affordability as the main issue. Others said they didn’t use the service enough, dropped accounts after free trials ended, or left because of price hikes. Some found better deals elsewhere, while a smaller group blamed poor customer service.

Changing Habits Since the Pandemic

During the early 2020s, households signed up quickly for new services. Subscriptions were seen as affordable alternatives to outside entertainment and shopping. That trend is fading. With tighter budgets and higher prices in 2025, many are cancelling accounts and weighing each service more carefully.

The findings show a shift. Subscriptions are still common, but people are more selective. Companies relying on recurring payments face greater pressure to show value, since customers are more willing to walk away.

Note: This post was edited/created using GenAI tools.

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• Sensitive Data Is Slipping Into AI Prompts, And Few Workers Realize the Risk

• New Research Warns Multitasking Leaves Employees Exposed to Phishing
by Asim BN via Digital Information World

Generative AI Becomes Two-Way Force, Altering Company Marketing and Consumer Product Searches

Generative AI is beginning to change how companies attract attention and how consumers decide what to buy. A survey of more than five hundred business leaders, conducted by Adobe Express team, showed that 34 percent had already received customer inquiries through AI-generated recommendations. Among those who gained business this way, the sales linked to these referrals made up 10.8 percent of their annual revenue on average. For nearly three in ten, the number of AI-driven leads passed fifty in a single year.

Some companies said these recommendations performed better than their older marketing methods. About 39 percent of leaders using generative AI for lead generation said the conversion rate was higher than traditional channels, while another 38 percent found it about the same. Only a minority saw weaker results. Chat-based platforms were the most widely adopted, with more than four out of five business leaders pointing to them as the primary tool for this type of marketing.

Investment in visibility within AI systems is also starting to take shape. Nearly a quarter of the executives surveyed said they were already spending more than ten percent of their marketing budgets on strategies that improve how their businesses appear in AI-generated suggestions. Technology companies were the most likely to commit at this level, with 38 percent of leaders in that sector saying they had made such allocations. The survey further showed that 48 percent of all business leaders expect to increase their spending on AI optimization in the year ahead, with almost six in ten tech leaders planning the same. Half of the total group believe AI systems will replace traditional search engines as their main source of leads within five years.

On the consumer side, the same survey reached just over five hundred people across different age groups. One in five respondents said they already use generative AI weekly to discover products, and younger consumers were leading the trend, with 28 percent of Gen Z doing so. Electronics were the most influenced sector, with 48 percent of shoppers saying AI affected their buying decisions in that category. Travel came next at 37 percent, followed by fashion at 25 percent.

The technology is not yet perfect. Almost six in ten consumers said the recommendations could be more accurate. Still, a smaller group of 13 percent said they would be willing to pay for premium versions of AI shopping tools that promise more reliable suggestions. Among Gen Z, that willingness rose to a quarter of respondents. Loyalty has also been tested, with 12 percent of consumers saying AI prompts had persuaded them to change brands, and nearly one in four Gen Z shoppers reporting the same.

Traditional search engines remain part of the picture, with 42 percent of respondents saying they still rely on them most often when searching for products. At the same time, 38 percent said AI-generated results felt more personalized, while a third said they were about the same as search. More than half, 52 percent, predicted that AI will overtake traditional search engines for product discovery within the next five years.

Consumer trust shows a mixed picture. A large majority, 63 percent, said they trust human reviews more than AI suggestions, while 30 percent said they trust both equally. For many, traditional search is losing ground because of problems like irrelevant advertising, overwhelming numbers of results, and concerns over authenticity. These frustrations are opening the door for AI to compete more strongly.

What emerges is a picture of businesses and consumers moving in parallel. Companies are adjusting their budgets to appear in AI-generated recommendations, and consumers are beginning to shift their product searches toward these tools. With both sides expecting greater use of generative AI in the years ahead, marketing and shopping habits may look very different by the end of the decade.

Read next: Sensitive Data Is Slipping Into AI Prompts, And Few Workers Realize the Risk
by Irfan Ahmad via Digital Information World

Sensitive Data Is Slipping Into AI Prompts, And Few Workers Realize the Risk

An employee sits at their desk, rushing to finish a proposal. Instead of drafting from scratch, they paste sections of a contract with client names into ChatGPT. Another worker, struggling with a login issue, types their company credentials into Gemini to “see what happens.” In both cases, sensitive information has just been handed to a third-party AI system.

Unfortunately, this type of credential leak is increasingly common. A new survey from Smallpdf of 1,000 U.S. professionals reveals how often employees are funneling confidential data into generative AI tools. For many organizations, it’s a threat that is rapidly growing inside everyday workflows.

The report highlights critical blind spots. For example, over one in four professionals admit to entering sensitive company information into AI, and nearly one in five confess to submitting actual login credentials. As businesses rush to embrace generative AI, these findings show that security, training, and policy are lagging behind adoption.

The Hidden Risks of Everyday AI Use

The past two years have seen generative AI tools like ChatGPT, Gemini, and Claude move from experimental curiosities to daily staples in the workplace. They’re used to draft emails, summarize meetings, and brainstorm strategy documents. But alongside convenience comes exposure. Professionals are pasting sensitive contracts, client details, and even login credentials into systems they don’t fully understand and aren’t entirely secure. Many professionals assume prompts are private. Yet, in reality, every entry can be stored, analyzed, or surfaced in ways beyond their control.

According to the research :

• 26% of professionals have entered sensitive company information into a generative AI tool.
• 19% have entered actual login credentials, from email accounts to cloud storage and financial systems.
• 38% of AI users admit to sharing proprietary product details or internal company financials.
• 17% say they don’t remove or anonymize sensitive details before entering prompts.
• Nearly 1 in 10 confess to lying to their employer about how they use AI at work.

Leake of sensitive information to AI is a widespread and growing concern. With over three-quarters of U.S. professionals using AI tools at least weekly, the line between efficiency and exposure has blurred. As adoption accelerates, organizations are learning that the true risks are unfolding inside everyday prompts.

When Your Prompts Become the Leak Surface

One of the most alarming aspects of this trend is that everyday employees are pasting sensitive material into AI chats. Contracts with real client names, internal financials, and passwords are routinely dropped into tools that may feel private but aren’t.

• Also read: Companies Struggle With a Hidden Cost of AI

What looks like harmless productivity can turn into data exposure at scale. The survey underscores the pattern: 26% of professionals admit to entering sensitive company information into AI tools, 19% have entered actual login credentials, and 17% don’t bother to anonymize details before they prompt. Many also misunderstand how these systems work, as 24% believe prompts remain private, and 75% say they’d still use AI even if every prompt were permanently stored.

The trust employees place in familiar interfaces like chat boxes, browser extensions, and built-in copilots has become a new attack surface. Without clear policies and training, convenience is becoming the newest attack vector, and routine prompts are becoming the breach.

Prompt Hygiene: The Achilles’ Heel

Most workplaces embraced generative AI before they built guardrails for it. That gap is where sensitive data slips out.

The survey reveals:

• 19% of professionals have entered actual login credentials into a generative AI tool.
• Of those, 47% entered a personal email, 43% a work email, 25% a cloud-storage login, and 18% a bank or financial account.
• 17% don’t remove or anonymize sensitive details before prompting.
• 24% believe their AI prompts are private, and 75% say they’d still use AI even if every prompt were permanently stored.
• 70% report no formal training on safe AI use, and 44% say their employer has no AI policy.

Traditional data-loss defenses weren’t built to monitor chat prompts in real time. Yet many organizations remain stuck, held back by policy gaps, training deficits, and trust in tools that feel safe but aren’t.

The Readiness Gap

Awareness is rising. Preparation isn’t. That’s the most troubling theme in the findings .

Just as AI use becomes routine, many basics are missing:

• 70% of workers report no formal training on safe AI use.
• 44% say their employer has no official AI policy; 12% aren’t sure, and 7% haven’t read the policy they do have.
• About 1 in 10 professionals have little to no confidence they can use AI without breaking rules or risking data.
• 5% have already faced a warning or disciplinary action for workplace AI use.
• 8% admit to lying about their AI use, and 7% used ChatGPT after being told not to.

This readiness gap is procedural and cultural. Policies lag behind practice, training lags behind demand, and trust in “helpful” tools is outpacing understanding of their risks. This is leaving employees anxious, inconsistent, and exposed just as AI becomes embedded in everyday work.

A Better Path Forward: From Ad-Hoc to Accountable

What does adapting to the prompt-leak problem look like? It starts with reframing AI use as a governed, privacy-first workflow. Treat every prompt like data in motion and design controls around it.

That could include phish-resistant guardrails for prompts, involving default blocks on credentials, client names, and financials. Additionally, this might include auto-redaction/anonymization before text reaches external models. Furthermore, enterprise controls ought to be prioritized over consumer chat apps. SSO, tenant isolation, retention may be switched off by default, and DLP can be set to scan for PII/IP in real time. Lastly, context-aware approvals identifying sensitive actions (e.g., summarizing contracts or uploading internal financials) can require additional validation or manager sign-off.

• Also read: Rising AI Pressure Pushes Professionals Back Toward Human Networks

Altogether, these controls point to a larger imperative: restructuring ownership so AI risk isn’t siloed. A cross-functional “AI governance guild” (e.g., security, legal, IT, and business leads) should co-own policies, training, and exception handling. Meanwhile, teams can pair AI with secure document workflows (redaction, watermarking, access controls). Distributing responsibility is quickly becoming essential for tools that evolve too quickly for linear, after-the-fact reviews.

A Problem of Technology and Trust

The damage isn’t limited to leaks or fines. It reaches into client confidence, data integrity, and long-term brand equity. The findings point to a different kind of churn: workers who assume prompts are private, leaders who haven’t set boundaries, and customers who recoil when their details show up in the wrong place. Routine AI use can feel like a privacy violation in slow motion when policies lag behind practice.

AI risk exploits software and certainty. Often, people stop trusting systems and companies when a friendly chat box stores contract clauses or a “helpful” assistant accepts passwords without warning. That trust is far harder to rebuild than any stack you can refactor. Once it’s gone, every login, form, and document share starts from a deficit.

Why Most Organizations Will Stay Exposed

If the dangers are so obvious, why do so many teams remain unprepared?

The data points to three overlapping blockers:

• Policy vacuum and training deficit. With 44% reporting no official AI policy and 70% receiving no formal training, employees default to improvisation in tools that feel safe but aren’t.
• Misplaced trust and poor prompt hygiene. Beliefs that prompts are private (24%), combined with weak redaction habits (17% don’t anonymize) and stubborn convenience (75% would use AI even if prompts were permanently stored), keep risky behaviors entrenched.
• Fragmented ownership and legacy workflows. AI use spreads across teams without clear governance, while document practices (contracts, financials, credentials) remain outside DLP and access controls, making copy-paste the path of least resistance.

These aren’t trivial obstacles, but they are solvable. As the costs of ungoverned AI mount, the price of inaction is climbing faster than most leaders expect.

Looking Ahead

The future of workplace AI will be defined by how quickly organizations shift from casual prompting to governed, privacy-first workflows. Leaders must move beyond ad-hoc guardrails and redesign how sensitive information is handled at the moment of prompt by treating every entry as data in motion, subject to redaction, routing, and audit.

At the root, leaders will be increasingly engaged in rethinking “productivity” in a world where contract snippets, client names, and credentials can be pasted into systems that store everything by default.

This also means resourcing the change. Give security, legal, and IT the mandate and budget to implement enterprise controls over consumer chat apps, deploy DLP that scans prompts, and roll out training that raises baseline literacy for every role. Asking teams to be safer with the same tools and no policy is how leaks become norms.

The story Smallpdf’s data tells is urgent: AI is already embedded in daily work, but the safeguards are not. The question now is whether organizations will modernize governance and prompt hygiene, or keep playing by pre-AI rules while sensitive details keep slipping through the chat box.

—

Methodology: This analysis draws on a September 2025 survey commissioned by Smallpdf of 1,000 full-time U.S. professionals across industries, job levels, and demographics, designed to understand how workers use generative AI and where sensitive information may be exposed in prompts and document workflows. Responses covered behaviors (e.g., anonymization habits, credential sharing), policy awareness, training, and tool usage frequency to illuminate risk patterns in everyday AI-assisted tasks. 

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• New Research Warns Multitasking Leaves Employees Exposed to Phishing

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by Irfan Ahmad via Digital Information World

New Research Warns Multitasking Leaves Employees Exposed to Phishing

Workers often switch between emails, meetings, and documents during the day. A study from the University at Albany shows that this constant juggling can reduce attention and make phishing attacks more effective. The research, published in the European Journal of Information Systems, connects heavy mental load with higher chances of missing signs of fraudulent messages.

Phishing emails remain one of the most common tools for cybercriminals. They aim to steal personal details, account credentials, or money. According to Valimail, around 3.4 billion phishing messages are sent every day. IBM estimates that an average incident costs businesses close to $5 million. The findings highlight how small drops in user awareness can translate into major financial risks.

Testing the Effect of Cognitive Load

The study involved close to 1,000 participants. Researchers asked them to complete email reviews while managing different levels of memory tasks. Results showed that when participants carried heavier mental loads, their ability to spot phishing attempts declined sharply. When the mental demand was lighter, accuracy improved.

The experiments suggest that memory and attention play a critical role in phishing detection. If workers are already focusing on difficult tasks, they may fail to notice details such as odd addresses or suspicious links. Divided attention reduces the level of scrutiny people apply to their inbox.

Role of Simple Reminders

The research also tested whether short prompts could help. A brief reminder before checking emails improved performance. Participants became more cautious when they were told that phishing attempts might be present. These reminders did not remove the effect of multitasking, but they reduced the impact.

Messages framed around rewards, such as offers or prizes, were the hardest to resist. People were more likely to believe them unless prompted to take care. In contrast, messages framed as threats, such as warnings about account lockouts, triggered more natural caution even without a prompt.

Training and Realistic Conditions

Many security training programs assume that workers are focused when phishing occurs. The study challenges that assumption. Real working conditions often include noise, interruptions, and simultaneous tasks. The findings suggest that training should reflect these distractions to prepare employees for realistic risks.

Simulated exercises with competing demands may help staff build habits that remain effective under pressure. Without this approach, lessons may not hold up when workers return to busy environments.

Practical Steps for Organizations

The authors highlight several measures that can reduce exposure to phishing:

• Introduce short alerts in email systems to encourage caution before clicking
• Design training that includes real-world distractions
• Teach staff how scammers use both threats and rewards to influence decisions

These steps reflect the idea that people are more vulnerable when attention is stretched thin. A momentary lapse can create an opening for attackers.

Financial Stakes

The cost of a phishing-related breach continues to rise. IBM estimates the average expense at nearly $5 million. Even small improvements in awareness can save companies large sums. Technology filters out many threats, but attackers continue to rely on human error because it cannot be fully automated away.

Shifting the Focus in Cybersecurity

The study shows why understanding human limits is central to defense. Multitasking changes how people judge information. Recognizing this effect can guide organizations in building stronger safeguards. Attention is a finite resource, and in digital workplaces it often gets divided.

The research offers a practical message: protecting information requires more than filters or policies. It requires systems and training that reflect how people actually work. When staff are busy, reminders and context-aware support can help them avoid costly mistakes.

Notes: This post was edited/created using GenAI tools. Image: DIW-Aigen.

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by Web Desk via Digital Information World

Meta Launches Paid Ad-Free Option for Facebook and Instagram in the UK

Meta is adding a subscription in the UK that lets people use Facebook and Instagram without ads. The rollout follows months of talks with the Information Commissioner’s Office, which pressed the company to give users a clear choice over data use and targeted advertising.

Prices and Accounts

On the web, the subscription is £2.99 a month. On iOS and Android it is £3.99 because of fees charged by Apple and Google. The fee covers all accounts linked through Meta’s Accounts Center. Extra accounts can be added for £2 a month on the web or £3 on mobile.

Oversight and Regulation

The ICO said the new model brings Meta closer to UK data protection rules. People can either keep using the platforms with personalised ads or pay to remove them. The regulator said it will keep an eye on how the change works in practice.

Meta is taking a different route in the UK compared with the European Union. In Europe, regulators fined the company €200m this year for failing to offer a lighter version of targeted ads. Subscription prices in the EU are also higher, starting at more than six euros a month.

Legal and Policy Background

The UK launch comes after Meta settled a case with campaigner Tanya O’Carroll, who argued her rights were breached when the company refused to stop using her data for advertising. Since then Meta has explored ways to give users an option to opt out, with the subscription being the result.

Legal analysts note that the ICO’s stance shows a split from the European Commission. In their view, the UK approach leans toward supporting business growth while still requiring some level of consumer protection.

Business Impact

Meta still defends targeted advertising as the foundation of its free services. The company says ads help people find products and give businesses an affordable way to reach customers. In 2024, its ad systems were linked to billions of pounds in economic activity and hundreds of thousands of jobs in Britain.

Now the choice is left with users, who can decide if an ad-free feed is worth the monthly cost.

Notes: This post was edited/created using GenAI tools. Image: DIW-Aigen.

Read next: UN Report Puts Global Tech Firms Under Spotlight for Links to Unlawful Israeli Settlements
by Irfan Ahmad via Digital Information World

UN Report Puts Global Tech Firms Under Spotlight for Links to Unlawful Israeli Settlements

The United Nations has updated its public database of businesses linked to Israeli settlements in the occupied West Bank. The new list identifies 158 companies. Some are local, but others are international names in the technology and travel sectors.

Travel Platforms Named

Several global booking platforms appear in the update, including Airbnb, Booking.com, Expedia, and TripAdvisor. These firms host and process reservations for accommodation inside illegal settlements. By doing so, they provide visibility and revenue to properties that the UN and the International Court of Justice consider unlawful.

The UN’s rights office said companies are expected to ensure their services do not contribute to rights abuses. The focus on high-profile travel platforms signals that digital intermediaries are part of the settlement economy, not only local construction or banking firms.

Telecom and Security Links

The database also highlights technology used in infrastructure and surveillance. Telecom operators Bezeq, Partner Communications, Hot Mobile, and Cellcom were included for supplying digital services to settlement areas. Motorola Solutions, along with its Israeli subsidiary, was listed for providing equipment used in security and monitoring systems.

• Also read: Microsoft Ends Israeli Military Unit’s Access to Cloud and AI Services Used in Palestinian Surveillance

Such companies form the digital backbone of settlements. Their networks and devices support daily operations and surveillance in disputed areas, which places them directly inside the scope of the UN’s assessment.

International Spread

Most of the companies listed are Israeli, but several come from abroad. Heidelberg Materials of Germany, which supplies building products, was included. Firms registered in Canada, China, France, Luxembourg, the Netherlands, Spain, the United Kingdom, and the United States also appear. Seven companies named in previous updates were removed after evidence showed they were no longer active in settlement-linked activities.

The database will continue to grow. More than 300 other firms remain under review, and further updates are expected.

Legal Context

The update comes after a 2024 advisory opinion from the International Court of Justice. Judges found that Israel’s settlement policies amounted to annexation of occupied territory and violated the Palestinian right to self-determination. The court also stated that states and businesses must avoid supporting activities that maintain such settlements.

The UN Human Rights Office applied its standard methodology, based on international business and human rights principles. The database does not provide a legal judgment, but it identifies companies where evidence showed involvement in one or more of ten specified activities linked to settlements.

Reputational Risk

For global firms, the consequences are reputational as much as legal. Travel platforms operate in highly visible markets where consumer perception matters. Telecom and surveillance firms may be less exposed to direct customer choice, but their role in building settlement infrastructure makes them subject to international scrutiny.

Other Sectors

While technology names stand out, many of the listed companies belong to construction, real estate, banking, mining, and retail. Together, these businesses sustain daily life in settlements. The UN says firms in such contexts have a duty to carry out due diligence, prevent harm, and offer remedies when their activities are linked to abuses.

Notes: This post was edited/created using GenAI tools. Image: DIW-Aigen.

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by Irfan Ahmad via Digital Information World

Privacy Nightmare: AI Turns Social Media Into Data-Hungry Giants, Study Warns

Large language models and machine learning are still new inventions by historical standards, but in everyday culture they already feel familiar. Collectively known as “AI,” these technologies have opened a Pandora’s Box of privacy concerns.

If AI is old-hat, then social media is ancient history. Yet the privacy risks associated with social media usage have only grown since the first modern social media platforms hit the scene around 20 years ago. These risks saw a stepwise increase in recent years, as so-called AI models began to be integrated into and trained on social media platforms.

Researchers at Incogni have prepared a new social media platform privacy ranking for 2025, expanding their criteria to include LLM and so-called generative-AI training concerns. This year’s ranking also stands out for taking a more nuanced approach than previous studies, expanding its scope to the end user’s experience in gathering and analyzing privacy-related information.

The study examined the top 15 social media platforms by monthly user count and ranked them according to 14 criteria across 6 categories:

• AI integration and training
• Privacy-related regulatory transgressions
• Data collection
• User control and consent
• Transparency
• User-friendliness

The results were appropriately weighted and combined to generate an overall privacy ranking, ordering the platforms according to the extent to which they pose a privacy risk to their users.

Somewhat predictably, Meta’s offerings (Facebook, WhatsApp, Instagram, and Facebook Messenger) and ByteDance’s TikTok round out the bottom of the ranking. Surprisingly, though, less popular platforms like Discord, Pinterest, and Quora fared relatively well.

As nice as it is to have an overall ranking like this, it’s the details of analysis that prove most useful in making informed decisions about which platform to trust, if any. For example, Pinterest, positioned as highly as it is overall, might not be the best option for a user who’s particularly concerned about data collection and sharing, as this is an area in which Pinterest performed worst of all.

The correlation between a platform’s overall position and its performance in the “AI and personal data” category is generally much stronger. This category covers criteria regarding whether a platform reserves for itself the right to train its own or other entities’ so-called AI on user data and whether it offers its users a mechanism for opting out of such data usage.

Other than introducing an AI-related category of assessment criteria, researchers added a subjective dimension to their analysis. Subjective but nonetheless quantified (via, for example, the Dale-Chall formula): the “user friendliness and accessibility” criteria capture how difficult it’s likely to be for a user to parse the relevant privacy policy documentation as well as the number of discrete steps a user would need to perform in order to delete their account.

This is an important part of analysis as it helps to ground the study in a typical user’s perspective. Concepts like “user friendliness” and “accessibility” depend heavily on the extent to which actual users can reasonably be expected to gather and process the information they need to make an informed decision. A privacy policy may well contain all the right information, but if it’s impenetrable to a reader with even a college education, then it fails to perform its core function, communicating salient details to an average user.

Darius Belejevas, Head of Incogni, had this to say:

There are no mainstream social media platforms that could, by any stretch of the imagination, be considered privacy-respecting. That said, social media platforms that respect users’ privacy do exist: Mastodon, PixelFed, and the ActivityPub protocol that allows them to federate are great examples, as are projects like Nostr and Matrix. But these platforms all share a common challenge: low uptake among everyday users. In other words: the network effect.

Continuing:

The reality is that people want the connection and distraction that mainstream social media platforms promise. Making privacy safeguards a desirable selling point is one thing we can all do to sway the market towards a more user-friendly future. The first step to doing that is understanding the privacy risks associated with those platforms as they are now.

This study brings to the fore a phenomenon that affects many aspects of this sometimes nebulous concept of “privacy”: its increasing and accelerating complication. As new as the personal-data exploitation boom of the early 2000s was, it now, in retrospect, looks like a decidedly simpler time. Going far beyond harvesting and analyzing personal data as it’s entered into and generated through interactions with these platforms, these companies are now following users around the web, surveilling their devices, and using anything they can find or infer to train various “AI” models.

All this represents a two-fold opening out of the privacy-risk landscape. On one hand, the streams of personal data going into these social media platforms have increased in throughput as they’ve multiplied in number. While, on the other hand, the streams of raw, processed, and inferred personal data leaving the same platforms have bifurcated time and again, and spread out far and wide.

Social media platforms are no longer limited to user interactions when it comes to satisfying their seemingly bottomless desire for personal information. Once they have a user’s data, they no longer limit themselves to exploiting it for marketing purposes. Data is sold to and bought from data brokers, disseminated through LLM outputs, and put to a far greater variety of uses... all often without the user’s informed consent and sometimes even contrary to their expressed wishes.

Studies like the one that resulted in Incogni’s social media privacy ranking are both a way to get the lay of this rapidly evolving landscape and a roadmap for choosing those routes that lead to a brighter future.

The full analysis (including public dataset) can be found here.

by Irfan Ahmad via Digital Information World