Skip to main content
Paul Welty, PhD AI, WORK, AND STAYING HUMAN

· artificial-intelligence

Bookmark: Exposed deepseek database revealed chat prompts and internal data

Bookmark: Exposed deepseek database revealed chat prompts and internal data

Uncover the risks of the exposed DeepSeek database, revealing critical cybersecurity flaws in AI platforms and the urgent need for enhanced data protection.

“The fact that mistakes happen is correct, but this is a dramatic mistake, because the effort level is very low and the access level that we got is very high,” Ami Luttwak, the CTO of Wiz tells WIRED.
Exposed DeepSeek Database Revealed Chat Prompts and Internal Data

The DeepSeek database incident underscores significant cybersecurity issues within emerging AI platforms. The Chinese AI firm DeepSeek unwittingly exposed a large database, including system logs, user prompts, and API keys, accumulating over a million records. The exposed data, discovered by security researchers at Wiz, revealed vulnerabilities due to minimal scanning requirements. Despite attempts to contact DeepSeek, the database was swiftly secured without disclosing whether any unauthorized party accessed the data.

The breach highlights the immaturity of DeepSeek’s security measures, akin to widely used open-source server analytics databases, yet displaying rudimentary security flaws. This incident further raises concerns about the security and operational integrity of AI models mimicking established systems like OpenAI’s, especially given DeepSeek’s structural similarities.

DeepSeeek’s rapid rise to popularity contrasts with its security inadequacies, triggering scrutiny from industry experts and regulators. The U.S. Navy’s caution against DeepSeek’s use reflects apprehensions over data privacy and national security, enhanced by its Chinese ownership. These events underscore the imperative for AI technologies to prioritize robust cybersecurity measures, preventing exposure from fundamental vulnerabilities like open databases, crucial in maintaining data integrity and user trust.

The agent-shaped org chart

Every real org has the same topology: principal, role-holder, specialists. Staff AI maps onto it, node for node, and the cost collapse shows up in the deliverables that were always just human-handoff overhead.

AI as staff, not software

Two frames for what AI is doing to work. The tool frame makes tools smarter. The staff frame makes roles unnecessary. Those aren't the same product, the same company, or the same industry.

Knowledge work was never work

Knowledge work was always coordination between humans who couldn't share state directly. The artifacts were never the work. They were the overhead — and AI just made the overhead optional.

The work of being available now

A book on AI, judgment, and staying human at work.

The practice of work in progress

Practical essays on how work actually gets done.

How do I get my dev team to adopt AI?

A stub on helping mixed-interest development teams find their own useful ways into AI.

Want to learn about agents? Talk to someone who ran an agency.

I spent 20 years running consulting engagements at Fortune 500 companies. Turns out that's the best preparation for running a fleet of AI agents ... because the problems are identical.

Your AI agents need a water cooler

We run a twelve-session AI fleet that coordinates through an IRC breakroom. A friend asked: why are you making AI agents act like humans? The answer turned out to be more interesting than the question.

Bookmark: Season’s smartest gift?—?a personal AI twin

Unlock the future of personal assistants with AI Twins that enhance privacy and enrich daily life—perfect for the holiday season. Explore now!

Article analysis: Why winning the AI race is critical for u.s. Military and economic dominance

Investing in AI is crucial for U.S. military and economic dominance. Discover insights from Mark Cuban on the race for technological supremacy.

Article analysis: Linkedin’’s AI misstep: The crucial role of transparency and communication in tech initiatives

Discover how LinkedIn's failure in transparency and communication sparked backlash over AI data use, highlighting crucial lessons for tech companies.