By Daniel Keller, CEO and Co-founder, InFlux Technologies
In April 2023, engineers at Samsung’s semiconductor division turned to ChatGPT for help. The motivation was simple: They wanted to debug code, streamline workflows and capture meeting notes more efficiently. In their eagerness to embrace generative AI, however, employees allegedly pasted fragments of proprietary source code and sensitive internal discussions directly into the chatbot. Within weeks, three separate leaks occurred.
What made this alarming was not just the breach but the significance of this mishap. Data entered into a public large language model (LLM) cannot be retrieved or deleted; similarly, Samsung’s crown jewels, its semiconductor designs, were exposed.
The company responded decisively. It banned ChatGPT (registration required) and similar AI tools from company networks and devices, while simultaneously racing to build internal AI solutions that could offer the same productivity boost without compromising security.
Beyond the mistakes of an employee, this was the story of a global enterprise discovering, in real time, how AI without guardrails can become a liability.
The Gold Rush
This cautionary tale sits against a backdrop of explosive adoption. According to McKinsey, 78% of respondents are already using AI regularly in at least one business function.
From automating marketing copy to drafting legal contracts, companies see LLMs as the new productivity engine. The allure is hard to resist: faster turnaround, fewer bottlenecks and an always-on digital assistant that scales instantly.
But this speed comes at a cost. In the race to implement AI, many organizations adopt tools faster than they establish rules. Enthusiasm often eclipses governance, and the consequences can be as severe as leaking proprietary data or worse.
The Lurking Dangers
The risks tied to LLMs aren’t always obvious. They don’t appear in a dashboard or a quarterly report. Instead, they surface quietly: leaked IP, flawed legal filings or regulatory violations.
Some of the most pressing vulnerabilities include:
• Data Leakage: As Samsung discovered, employees may feed confidential information into third-party models, creating irreversible exposure.
• Hallucinations: LLMs can fabricate answers with supreme confidence. In 2023, two lawyers in New York submitted a legal brief generated by ChatGPT (registration required) that cited entirely fake cases. The court sanctioned them, and their firm’s reputation suffered a significant blow.
• Compliance Gaps: Data privacy rules such as GDPR and HIPAA require strict controls. Feeding personal or health data into an LLM could expose companies to fines or lawsuits. Italy even temporarily banned ChatGPT in 2023 over concerns about privacy and data collection.
The common thread is simple: AI amplifies both productivity and risk. Without human oversight, it is just as effective at creating vulnerabilities as it is at solving problems.
Guardrails For The Generative Age
Samsung’s ban may have been drastic, but it illustrates a fundamental truth: Enterprises need guardrails to use generative AI safely. These guardrails are not about stifling innovation; they are about ensuring that innovation doesn’t erode trust, IP or compliance.
A strong framework includes:
1. Access Control
Not every employee needs a direct line to an AI system. Limit who can use AI tools and under what circumstances. Samsung, in the aftermath of its leak, restricted input sizes to just 1,024 bytes as an emergency measure to reduce risk.
2. Data Classification
Clear rules must define what data is safe for AI input. Proprietary source code, financial records or personal information should be off-limits. This requires building a culture where employees instinctively ask, “Should I be sharing this with an external system?”
3. Red Teaming AI Systems
Much like penetration testing in cybersecurity, enterprises should red team their AI systems, intentionally stress-testing them against adversarial prompts, jailbreak attempts and data poisoning. This proactive approach reveals vulnerabilities before attackers exploit them.
4. Policy And Training
Technology cannot replace awareness. Regular training sessions, clear guidelines and scenario-based education ensure employees understand both the benefits and dangers of generative AI.
Why Humans Still Matter
Even with policies and technical safeguards, the human factor remains a decisive factor. AI cannot secure itself. Without human-in-the-loop oversight, enterprises risk overreliance on outputs that may be biased, false or legally hazardous.
The legal industry provides a stark example. Beyond the New York case, courts are now issuing standing orders requiring attorneys to disclose whether they’ve used AI in filings. Judges are signaling that human judgment is non-negotiable (registration required), no matter how advanced the tool.
AI is a partner, not a substitute. Its value maximizes only when combined with human ethics, scrutiny and responsibility.
The Competitive Edge Of Security
Securing generative AI is not just a defensive necessity; it’s a strategic advantage. Here’s why.
• Customer Trust: Clients are more likely to do business with organizations that protect their data rigorously. A 2023 PwC survey found that 85% of consumers won’t engage with companies if they have concerns about security practices.
• Investor Confidence: Boards and shareholders reward companies that balance innovation with risk management. According to a World Economic Forum report, firms with robust cybersecurity practices are more likely to outperform peers in long-term valuation.
• Regulatory Readiness: With frameworks like the EU AI Act moving closer to full implementation, compliance today prevents penalties tomorrow. The Act not only imposes fines for non-compliance but also creates new standards for “high-risk” AI systems. Companies that prepare early stand to win contracts in regulated industries, such as finance and healthcare, while laggards risk being excluded from lucrative markets.
Rounding Up
Samsung’s experience is not an outlier; it is a glimpse into the new reality of enterprise AI. The incident was avoidable, but it was also instructive: Guardrails are no longer optional.
On its own, AI offers undeniable promise. It can write, code, summarize and assist at speeds no human team can match. But without clear boundaries, it becomes a liability waiting to surface in the worst possible way, through a leak, a lawsuit or a reputational crisis.
The path forward is clear: Pair every innovation with security. For enterprises, the challenge is no longer whether to adopt AI and LLMs but how to use them responsibly.