News

Cloud Security Org Weighs In on DeepSeek AI Just as Data Leakage Reported

• By David Ramel
• 02/03/2025

The Cloud Security Alliance (CSA) has weighed in on the debut of revolutionary and controversial DeepSeek AI just as a report was published on data leakage from the platform.

The CSA, a non-profit organization that promotes best practices for cloud security, issued its take on the breakthrough it said was rewriting the rules of AI development and provided strategic implications along with action items for the changed AI space.

On the very same day, Jan. 29, cloud security company Wiz reported its research had uncovered exposed DeepSeek database leakage that left open sensitive information such as chat history.

The good news about the latter is that it has been fixed.

"Wiz Research has identified a publicly accessible ClickHouse database belonging to DeepSeek, which allows full control over database operations, including the ability to access internal data," the Wiz post said. "The exposure includes over a million lines of log streams containing chat history, secret keys, backend details, and other highly sensitive information. The Wiz Research team immediately and responsibly disclosed the issue to DeepSeek, which promptly secured the exposure."

Data leakage is just one of the concerns raised by the debut of the new AI tech that performs among the best large language models (LLMs) despite costing a fraction of the cost to train and develop. That combination of price/performance rocked the industry.

However, amid that disruption came controversy about using the technology, which was banned by many orgs.

"This achievement has forced a complete reassessment about what it takes to build advanced AI systems," said the CSA, which noted the existing conventional rules that were challenged by DeepSeek.:

• Massive GPU clusters (16,000+ chips)
• Billions in investment
• Large teams of experienced AI researchers
• Years of iterative development

The CSA also summarized the implications of the DeepSeek AI breakthrough in five key areas. "The most significant aspect of DeepSeek's achievement?" the CSA said. "It systematically dismantled what experts once considered insurmountable technical moats in AI development." Those moats include:

• Data Advantage Myth: The assumption that only companies with massive proprietary datasets could build competitive models has been challenged. DeepSeek achieved state-of-the-art performance without the vast data repositories of tech giants.
• Compute Infrastructure: DeepSeek upended the belief that cutting-edge AI required massive data centers and specialized infrastructure. DeepSeek's efficient architecture achieved superior results with just 2,048 H800 GPUs, a fraction of what competitors use.
• Training Expertise: DeepSeek disproved the notion that only large teams with years of specialized experience could train advanced models. DeepSeek's innovative approaches to model architecture and training have achieved comparable or superior results with a smaller, younger team.
• Architectural Innovation: DeepSeek's Mixture of Experts (MoE) approach and efficient parameter activation system has demonstrated that architectural innovation can overcome supposed resource limitations.
• Cost Barriers: DeepSeek shattered the assumption that frontier AI development required billions in investment. DeepSeek's $5.58 million training cost for their V3 model represents a paradigm shift in cost efficiency. However, total development costs were higher.

The report listed many strategic implications and action items around topics including rethinking resource allocation, team structure revolution, training methodology and more. It also provided "novel insights" about architectural innovation priority, data strategy pivots, and the acceleration of development timelines.

Immediate action steps, meanwhile, included:

• Audit Current Approaches:
  • Review infrastructure spending
  • Assess team structure efficiency
  • Evaluate development methodologies
• Restructure Development Programs:
  • Implement rapid prototyping for architectural innovations
  • Establish efficiency metrics and goals
  • Create innovation-focused teams
• Strategic Realignment:
  • Shift focus from scale to efficiency
  • Prioritize architectural innovation
  • Develop new success metrics based on efficiency

"The future of AI development lies not in amassing more resources, but in using them more intelligently," the CSA concluded. "Organizations need to pivot away from a 'more is better' approach. Instead, they must prioritize efficiency, innovation, and smart resource use."