Why Your Company’s Data Doesn’t Belong in a Public LLM

1. Introduction

Not long ago, storing your data in the cloud seemed like the safest, most scalable choice. Today, sending sensitive information, trade secrets, customer datasets, IP, internal metrics, into public Large Language Models (LLMs) poses a serious, often underappreciated risk. Once your data enters a “public” AI infrastructure, you lose direct control over it, and you may never fully know how it’s being used or shared.

In fact, industry analyses have flagged this danger: submitting data to cloud-based LLMs can expose your organizational secrets, customer PII, or competitive insights, sometimes without your immediate awareness. Alphalect The European Data Protection Board’s 2025 report also underscores how privacy leakage can occur across the AI lifecycle, from data ingestion to inference outputs, especially in shared or black-box environments. European Data Protection Board

In this article, we’ll explore why the cloud-LLM model is more exposed than most companies assume, what the risks look like in practice, and how you can take control of your data again (with Ember as a possible solution).

2. Market Context

The promise of AI-as-a-service (AIaaS) and public LLM APIs is seductive: instant access, lower startup costs, seamless scaling. Many teams default to cloud-based models because “someone else handles infrastructure, tuning, scaling.” But this convenience comes with hidden compromises.

Recent reports show that public LLMs often ingest or cache user inputs (depending on vendor policies), which can then be used for training, analysis, or internal optimization, sometimes without explicit consent or transparency. Matillion+2Tigera - Creator of Calico+2 Further, security vulnerabilities in model-serving layers, prompt injection attacks, or misconfigurations can expose sensitive data. Coralogix+2Legit Security+2

Regulators are catching up: privacy authorities are increasingly scrutinizing how AI providers handle submitted data, with an eye toward data sovereignty, lawful processing, and re-identification risks. IBM+1 Meanwhile, organizations in regulated sectors (finance, health, defense) are being forced to rethink whether public LLMs meet compliance requirements.

In short, the trade-off between cloud convenience and data safety is tilting. Many companies are realizing that they can no longer treat public LLMs as “safe black boxes.”

3. Public vs. Private LLM

When we refer to a public LLM, we mean a model hosted by a third-party AI service, you send requests (text, embeddings, prompts) over the network, and receive outputs. That infrastructure is shared (multi-tenant), often opaque, and may ingest or store parts of your inputs.

In contrast, a private or on-prem (or device-local) LLM keeps your data and processing within your environment. Ember is built on that premise: the device runs models locally or in your private infrastructure, so data never leaves your control unless you choose to share it.

The myth is: “public LLM = instant value, zero risk.” The reality is: public LLMs carry structural vulnerabilities, from data leakage, inference attacks, prompt injection, to model theft. A recent paper found that adversaries can extract forgotten training data via fine-tuning APIs, even when the dataset was supposed to be “erased.” arXiv Moreover, public models might repurpose or log inputs in ways you never agreed to.

Hence, the distinction matters deeply when you're handling private, sensitive, or proprietary data.

4. Key Risks & Impacts

A. Loss of Control & Data Leakage

Once a prompt or document is submitted to a public LLM, you no longer have absolute control over how that data is stored, reused, or shared internally. Alphalect+2Matillion+2 In worst-case scenarios, private code, internal memos, strategic roadmaps, or IP might leak via inference or output exposures.

B. Inference Attacks & Model Extraction

Even if the model doesn’t intentionally retain your data, attackers can launch membership inference or attribute inference attacks to probe whether specific data came from your input sets. The EDPB’s report warns of these risks along LLM pipelines. European Data Protection Board Beyond that, model extraction attacks (reconstructing model internals) have been demonstrated against publicly accessible LLM endpoints. Tigera - Creator of Calico+1

C. Prompt Injection & Behavioral Manipulation

Public LLM endpoints are vulnerable to prompt injection, adversarial inputs that manipulate model behavior, bypass restrictions, or leak data. The concept is well documented: attackers craft prompts to influence or extract hidden content. Wikipedia Because public models share infrastructure, boundary enforcement is more complex and less auditable.

D. Regulatory & Compliance Risk

If your data includes personal or sensitive information (customer records, health data, financial data), sending it to a public LLM can breach GDPR, HIPAA, or other privacy laws — especially if you don’t retain control over processing, retention, or deletions. The misuse or re-sharing of data without transparency could expose you to fines or regulatory scrutiny. European Data Protection Board+1

E. Reputation & Trust

Imagine a competitor or journalist prompting your private documents and seeing your roadmap, client lists, or undisclosed metrics. That’s not hypothetical: several security incidents involving AI providers leaking internal data have made headlines. WIRED+1 Loss of customer trust and reputation damage can be expensive, sometimes irreparable.

5. On-Device Approach

Adopting Ember-style architecture isn’t free of trade-offs. Recognizing challenges helps you mitigate them:

• Compute & Infrastructure Costs, Running large models locally or privately may require powerful hardware, memory, and energy consumption.

• Model Updates & Maintenance, You must manage updates, patches, and security hardening yourself or through partners.

• Ecosystem Integration, Some SaaS or third-party plugins expect cloud-hosted AI endpoints; bridging that gap can require development effort.

• Usability & Latency Trade-offs, If compute is limited, responses may be slower compared to ultra-scaled cloud models; careful model choice is essential.

But these challenges are increasingly manageable, especially given the cost of a data breach.

6. How to Approach It

Step 1: Classify Your Data Sensitivity

Not all data is equal. Segment what’s truly sensitive (customer PII, proprietary IP, legal docs) vs. what’s low-risk (public articles, aggregated non-sensitive logs). Restrict public LLM use only to lower-tier data or scrubbed inputs.

Step 2: Use Client-Side Filtering / Gatekeeping

Use a lightweight local filter or anonymizer (a “gatekeeper”) that strips out or masks sensitive attributes before sending anything out. Recent research suggests combining a local filter + remote LLM reduces leakage without noticeable client impact. arXiv

Step 3: Prefer Private / On-Prem / Edge Models

Run models within your firewall, on devices like Ember, or in private cloud partitions not exposed to public APIs. This ensures full control over data flow, retention, and security.

Step 4: Apply Rigorous Controls

• Enforce role-based access

• Log all prompts and metadata (audit trail)

• Encrypt stored data both at rest and in transit

• Isolate model serving environments

• Regularly test for prompt injection, inference attacks, and adversarial queries

Step 5: Monitor & Adapt

Continuously monitor usage, failed prompts, abnormal outputs, and access attempts. Rotate keys, fine-tune models, and update security layers.

Step 6: Educate & Govern

Train your teams about the risks of uploading internal files to public AI tools. Establish governance around what can/cannot be processed in public models.

7. Case Illustrations

Success: Local-First SaaS Team
A SaaS startup handled customer support data entirely on-device for a beta release, using a private LLM for summarization. Because no customer inputs ever left the system, they avoided leakage risk and built trust with early users, a key selling point in pitching to regulated clients.

Caution: Public LLM Overreach
A marketing agency used a public LLM to process campaign data and accidentally revealed sensitive client performance metrics via shareable links. Worse, the LLM’s internal logs cached fragments of user data, later surfaced in a leaked debugging dump, a serious trust breach.

These real-world narratives reflect risks documented in security analyses of cloud LLM misuse. Alphalect+2Coralogix+2

8. Conclusion

The cloud has brought enormous value and convenience, but for AI workflows involving sensitive or proprietary data, it has also introduced a hidden vulnerability. Public LLMs can leak, misuse, or expose data in ways many organizations don’t anticipate.

By contrast, embracing private or on-device models (like Ember) restores control, visibility, and trust. The upfront complexity is dwarfed by the cost of a data breach, reputational, legal, financial. The future of AI in business won’t be about “cloud or bust”, it will be about where and how your data is processed, with security at the center.

If you want help mapping which parts of your operations are safe to run on public LLMs, and which belong behind your own firewall—or on Ember—feel free to reach out or subscribe for deeper guides.

References

• The (not so) Hidden Risks of Using Cloud-Based LLMs — Alphalect, 2025 Alphalect

• AI Privacy Risks & Mitigations – Large Language Models — EDPB, 2025 European Data Protection Board

• LLM Security Risks and Best Practices — LegitSecurity Legit Security

• Top AI and Data Privacy Concerns — F5 F5, Inc.

• The Security Risks of Using LLMs in Enterprise Applications — Coralogix, 2024 Coralogix

• Public vs Private LLMs: Secure AI for Enterprises — Matillion, 2025 Matillion

• Prompt Injection (Wikipedia / common reference) Wikipedia

• The Janus Interface: Privacy Risks via Fine-Tuning — Chen et al, 2023 arXiv

• Guarding Your Conversations: Privacy Gatekeepers — Uzor et al, 2025 arXiv

• DeepSeek Database Exposure / AI Security Incidents WIRED+1