AI wrote the code. Hackers wrote the consequences. That’s the grim reality many organizations are now facing as they rush to embrace AI‑assisted development without proper oversight. In late July 2025, Tea, a women‑only dating advice and experience‑sharing app, became the center of a major cybersecurity incident that exposed the dangers of AI‑assisted software development when human oversight is lacking.
Tea was designed as a space where women could anonymously share stories, flag red‑flag behavior in men, and connect over personal experiences. Unfortunately, the same design decisions that made the app appealing also made it a prime target for attackers. Around 72,000 images were exposed in the breach, including approximately 13,000 verification selfies and government‑issued ID photos, alongside 1.1 million private messages. Many of these messages contained deeply sensitive disclosures involving sexual assault, infidelity, and other intimate details. Once the data began circulating on 4chan and other forums, the privacy and safety of thousands of users were irreparably compromised.
Initial investigations revealed that Tea’s data was stored in a legacy Firebase environment that lacked proper security controls. Images and private messages were not encrypted, access permissions were misconfigured, and there was little evidence of effective logging or monitoring. The breach was not the result of an advanced nation‑state campaign or sophisticated zero‑day exploitation. Instead, it was a textbook example of basic security hygiene being neglected. The failure stemmed from a broader trend in software development known as “vibe coding,” where small teams rely heavily on AI tools like ChatGPT or GitHub Copilot to generate code rapidly, with minimal human oversight and often without the rigor of a structured software development life cycle (SDLC).
AI can be an incredible accelerator. It can generate application code in minutes, suggest fixes, and even write test scripts. But AI does not—and cannot—replace the core responsibilities of human engineers and security professionals. It cannot automatically apply threat modeling to anticipate how a malicious actor might exploit a system. It cannot make the judgment calls necessary to classify sensitive data properly, ensure encryption is applied at every layer, or confirm that an app meets regulatory requirements such as GDPR or HIPAA. And while AI can help produce unit tests, it cannot replace penetration testing, manual code review, or red‑team exercises that catch subtle but critical vulnerabilities. In Tea’s case, the rapid release cycle, fueled by AI‑assisted development, meant the app scaled to thousands of users while the security foundation remained dangerously thin.
Traditional SDLC processes exist for a reason. In a secure development workflow, teams begin with requirements gathering and threat modeling, identifying what data is sensitive and how attackers might target it. Design and architecture reviews ensure proper system isolation and clear data access boundaries. During implementation, developers must still conduct human code reviews, penetration testing, and validation of encryption and access controls. Deployment only happens once these controls are verified, and post‑launch monitoring must remain a top priority. When organizations shortcut these steps because AI makes development “easy,” the result is predictable: vulnerabilities proliferate, attackers find the weak points, and end users suffer the consequences.
Tea’s breach illustrates this perfectly. Sensitive images and private messages were left unencrypted and exposed in a misconfigured database. Legacy data that should have been purged was retained unnecessarily, amplifying the breach’s impact. Without robust monitoring and logging, the team had limited visibility into how and when attackers accessed the data, which delayed detection and slowed response. Human security gates were bypassed in the pursuit of speed, and the cost to users was enormous.
The lesson is clear: AI is a powerful tool, but it is only one part of the development process. Treating AI as a complete replacement for disciplined engineering and security practices is an invitation to disaster. Organizations that embrace AI in their development pipelines must pair that acceleration with equal, if not greater, attention to human oversight and rigorous testing. This means auditing AI‑generated code for security flaws, encrypting sensitive data at every stage, and conducting regular penetration tests and breach simulations. It also requires aggressive legacy data management to minimize exposure in the event of a compromise and periodic external security reviews of cloud and app configurations before public launch.
The Tea breach is not just a story of one app’s failure—it is a warning to every organization tempted to rely solely on AI‑driven development. In cybersecurity, there are no shortcuts. User trust is earned through diligence, and that trust can be shattered overnight when sensitive data is exposed. AI can write code, but it cannot keep your users safe without the watchful eye and careful judgment of human engineers and security teams.
If this incident has caught your attention and you want to explore how AI is reshaping the cybersecurity landscape, I dive deeply into these topics in my book, AI Disruption: How AI Is Reshaping Cybersecurity, Privacy, and Compliance. The book examines real‑world cases like Tea, the dual use of AI by attackers and defenders, and practical strategies for integrating AI into secure and compliant development pipelines. AI is here to stay, but so is the need for rigorous human oversight. The organizations that understand this balance will be the ones that keep their users safe and their reputations intact.
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