Why “More Data” Is Now a Liability for Platforms

Key Takeaways:

• Storing large amounts of personal information has become a growing source of liability for platforms. Older records are difficult to track, secure, and retire, and they expand long-term risk.
• AI has increased the impact of data exposure. Even small or outdated fragments can be turned into synthetic identities, deepfakes, or targeted attacks.
• Some governments and companies are testing lighter data practices, but many still overlook key privacy concerns. Privacy-first identity models will play a larger role as platforms look for ways to reduce long-term exposure.

The Hidden Cost of Collecting Too Much Information

Data has always played a major role in online services. For years, many platforms believed that keeping more information would prevent fraud and make future checks easier. If a company held enough records, it could confirm someone’s identity at any point and resolve disputes quickly.

That approach is showing clearer limits. IBM’s 2024 breach report found that 40 percent of incidents involved data spread across multiple environments and more than one third involved shadow data stored in unmanaged locations. Once information enters a system, it moves across backups, services, and tools, making it harder to account for over time. Each additional record adds work for security teams and increases the surface attackers and auditors may examine.

The real risk comes from the information that stays in a system long after it is needed. As the amount of stored data grows, so does the responsibility that comes with safeguarding it. The next sections explain why this responsibility keeps growing, how modern threats amplify these risks, and what platforms can do to verify users without holding on to more data than they need.

How Personal Data Became a Liability Instead of an Asset

Stored personal information is now treated as something that carries ongoing risk. Regulators, insurers, and courts expect platforms to justify why they hold certain data and to protect it with strong safeguards. This has made data liability a central part of compliance discussions. Once a record is stored, it requires documentation, oversight, and security for as long as it remains in the system.

Recent incidents show how this responsibility can grow. AT&T reported that older customer records appeared on a breach forum despite being years out of use. A compromised contractor account at Discord exposed internal materials. DNA-testing service GEDmatch faced an incident that revealed profile details despite user restrictions. These examples illustrate how older data can resurface long after its intended purpose.

Platforms that keep personal information must also meet strict expectations:

• secure and encrypt data across all environments
• document why each category of data is retained
• monitor systems for unusual activity
• remove information on schedule
• demonstrate compliance to regulators and partners

As these expectations expand, so do the costs. Information that once seemed harmless becomes harder to maintain and more complicated to justify.

How AI Has Increased the Risks of Storing Identity Data 

AI has changed how attackers use exposed information. Tools that once required significant manual work now automate these actions at speeds that were not possible before. As a result, stored information can be misused in more ways and for much longer periods.

These changes are most visible in a few key areas.

1. Partial Data Now Enables Full Identity Profiles

Small fragments of information can now be assembled into realistic profiles. Older addresses, outdated ID numbers, or stray credentials can be combined into synthetic identities that pass basic checks. Attackers also use AI to generate fake documents that match these fabricated details and appear credible at first glance.

2. Deepfake Tools Make Impersonation More Realistic

Readily available models can create voice and face imitations using brief samples. Attackers have used these tools to impersonate executives, request transfers, or bypass weak liveness checks. As biometric samples continue to circulate online, impersonation attempts become harder to detect.

3. Targeted Attacks Have Become Far More Precise

Phishing and social-engineering attempts now draw on leaked information to mimic a person’s writing style or personal details. Messages feel more credible, which increases the likelihood of someone sharing credentials or approving fraudulent requests. Automated tools also test stolen logins across multiple services within minutes, making account takeovers easier to carry out at scale.

4. Anonymized Data Is Easier to Reassemble

Information stripped of direct identifiers is no longer guaranteed to stay anonymous. When datasets overlap, AI can uncover patterns that reconnect records to real individuals. Regulators such as the FTC and ICO warn that re-identification is becoming more common, especially when anonymized data intersects with public or leaked sources.

5. Older Breaches Remain Dangerous Much Longer

AI can analyze older breach dumps, fill in missing fields, and merge them with new leaks. Even outdated records stored by a platform can contribute to new incidents, long after they stop serving any useful purpose.

Legal and Financial Consequences of Holding Unnecessary Identity Data

Storing personal information now creates challenges that extend beyond security. Many organizations still collect large amounts of data as part of their normal operations. A 2023 global survey found that 79 percent of companies gathered personal data on individuals in North America, Western Europe, and other developed regions. This habit continues even as regulators, insurers, and business partners place more pressure on companies to limit what they keep. These expectations now influence how identity systems are built and how verification steps are designed.

These pressures are showing up across a few major areas:

1. Regulatory Penalties Are Increasing

Regulators are treating long-term retention as a privacy issue on its own, not just a security matter. Companies in finance, social platforms, and consumer services have faced penalties for holding documents, biometric samples, or account files past their approved timelines. Updated standards such as NIST SP 800-63 Rev.4 and eIDAS 2.0 reinforce this direction by encouraging lighter storage models and discouraging large centralized repositories of sensitive information. These actions show that regulators now expect companies to justify every category of data they keep.

2. Legal Actions Now Focus on Retention Decisions

Courts are seeing more cases that challenge whether certain information needed to be stored at all. Plaintiffs argue that unnecessary retention creates avoidable risk, even when no breach occurs. Regulators have taken similar positions by penalizing companies for improper handling of identity documents or failing to enforce their own retention schedules. These actions make it clear that data liability exists even in the absence of unauthorized access.

3. Insurance Costs Are Rising

Cyber insurers are raising standards for companies that hold identity data. Many now require clear, enforced retention rules and full visibility into shadow data. Organizations that store more than they need often face higher premiums and narrower coverage, since insurers view excess data as a sign of higher exposure and potential claims.

4. Operational Costs Continue to Grow

Even when older information is not actively used, it still requires protection. Companies must maintain encryption, monitor systems, oversee access controls, and secure backups containing historical data. As systems change over time, older records become harder to track and manage. These operational demands continue year after year, turning unused data into an ongoing financial burden.

Modern Identity Verification Methods That Avoid Permanent Storage

As more platforms look for ways to avoid storing unnecessary personal data, new verification methods are helping them confirm important details without keeping sensitive information. These approaches allow companies to complete identity checks while greatly reducing what enters their systems.

1. One-Time Verification Tokens

These short-lived confirmations let platforms verify age, eligibility, or trusted status without storing the underlying documents. Because the tokens expire quickly and do not contain raw personal data, they create far less risk if intercepted or reused.

2. Ephemeral Cryptographic Proofs

Cryptographic techniques let users prove something without sharing the actual data behind it. Someone can confirm they are over a certain age or hold a valid credential while keeping the underlying files on their own device. The proof disappears once the check is complete, so the platform does not take on long-term exposure.

3. On-Device Verification

Biometric checks, credential storage, and validation steps can happen directly on a user’s device. The platform receives only a simple confirmation result. This avoids large data repositories, reduces the impact of a breach, and lowers regulatory concerns because sensitive material never leaves the device.

4. Automatic Expiration and Revocation

Some verification systems issue credentials that automatically expire or can be revoked by users or trusted issuers. This prevents outdated data from lingering in a system and reduces how much information a company needs to protect over time.

What Platforms Should Do Now to Reduce Data Risk

These verification methods are already influencing how modern systems are being designed. Platforms that want to lower long-term exposure can start with a few practical steps:

1. Map what is stored: Companies should take a full inventory of the personal data they hold across all environments. This includes older files, forgotten backups, and anything stored in places teams may no longer monitor.
2. Remove records that are no longer required: Retention schedules should work in practice, not just on paper. Clearing out outdated or unused information reduces risk and helps keep systems more manageable.
3. Automate expiration and revocation: Whenever possible, identity records, credentials, or verification results should have built-in expiration or require renewal. This helps prevent unnecessary buildup and keeps data current.
4. Adopt verification methods that avoid permanent storage: Techniques such as on-device processing, selective disclosure, and short-lived proofs help verify users without creating long-lasting records that become a liability over time.
5. Use data minimization as a trust signal: People and businesses increasingly expect platforms to avoid collecting unnecessary information. Clear, well-communicated data practices can strengthen trust and make approvals smoother.

How Governments and Major Platforms Are Reducing Identity Data Storage

Governments and major technology companies are starting to rethink how much personal information they store. The pace of change varies across regions and industries, and many systems still rely on older models that keep more data than necessary, but interest in lighter, privacy-focused approaches is growing.

In the public sector, some early changes are starting to appear. The European Union’s eIDAS 2.0 framework supports tools that let people prove a single detail without handing over full documents. The United Kingdom is testing services that only ask for the information needed for one specific task. Countries building newer digital identity systems, such as India and Singapore, are also exploring shorter retention periods and approaches that keep more information on personal devices instead of in large central databases. These updates differ in maturity, but they show that many governments are beginning to recognize the risks that come with storing too much information.

Technology companies are making similar shifts. Apple continues to move more verification steps onto devices, and its digital ID features follow the same pattern by keeping identity details on the device rather than storing them on company servers. Mastercard is adding attribute-based checks to some of its identity products so users do not have to upload full documents. These changes help limit how much information flows through their systems, though adoption and consistency still vary.

As more organizations see the consequences of storing large amounts of personal information, interest in smaller data footprints continues to grow. For these changes to have real impact, platforms will need to be clear about how they collect, use, and delete information. The steps being taken are still early and not always aligned, but they reflect a growing effort to handle identity data with more care and less long-term storage.

Conclusion

Most platforms still collect and store more personal information than they need, even as the risks tied to this data continue to increase. Verification does not require this level of retention, and keeping unnecessary information makes systems harder to secure and more expensive to maintain. Smaller data footprints reduce exposure and simplify compliance, but reaching that point will require clearer practices and more consistent adoption. If companies continue storing more information than necessary, the legal, financial, and security consequences will only intensify.