Why Security Matters in GenAI: Lessons from  Iron Bank Deployment

In the rush to adopt Generative AI, one critical factor often gets buried under promises of scale and hype: security. Many of the largest AI companies want to be everything at once — data provider, model builder, SaaS vendor, and even regulator of the ecosystem. This concentration of power may deliver impressive demos, but it rarely delivers the security, privacy, and compliance that sensitive industries demand.

At Pangeanic, we have taken a different path. Our recent deployment of a secure AI translation engine for Veritone, wrapped in the U.S. Department of Defense’s Iron Bank environment, proves that trustworthy GenAI must be built with security-first principles — not simply massive scale.

And in this rush to adopt Generative AI, one critical factor often gets buried under promises of scale and hype: security. Many of the largest AI companies aim to be and do everything at once: masters at writing code, model builders, SaaS vendors, co-pilots summarizing content, general translators, psychologists, legal advisors, doctors, and personal companions. They even provide gardening tips. And the selling point is "more productivity" or "let's get rid of humans". Large Language Models have been mistakenly equated with strong "AI" and particularly AGI (Artificial General Intelligence), when in reality, they are massive knowledge libraries with language fluency. These models are trained decoders that access the web, at best, to create immediate RAG-based summaries of information.  Developers have also become regulators of the ecosystem in a dangerous manner.

Read more: What is AI and what is Artificial General Intelligence? (AGI)

This concentration of power may deliver impressive demos, and it will boost some electricity companies' revenues. Still, it rarely provides the security, privacy, and compliance that enterprises, communities, governments, and regulated industries demand.

At Pangeanic, we’ve taken a different path; it is a path that takes us to task-specific small models with privacy in mind (more below about Gartner's predictions). Our recent deployment of a secure AI translation model for Veritone, wrapped in the U.S. Department of Defense’s Iron Bank environment, proves that trustworthy GenAI must be built with security-first principles,  not simply massive scale. Above all, it demonstrates that private GenAI is possible.

Yes "public" Generative AI is transforming industries at an unprecedented pace. From drafting emails to writing complex code, the power of Large Language Models (LLMs) is undeniable. In the rush to adopt this technology, many organizations are turning to the big-name, "do-it-all" tech giants. The convenience is tempting, but it begs a critical question: Where is your sensitive data actually going?

When you use a public, general-purpose AI model, you're often sending your proprietary information (product roadmaps, financial data, customer lists, internal strategies) to a third-party server. These large AI companies may use your data to train their future models, potentially exposing your secrets to the world or even to your competitors. It's like shouting your company's most confidential plans in a crowded public square.

This is not just a theoretical risk. It's a direct threat to your competitive advantage, data sovereignty, and regulatory compliance.

What is "Iron Bank Standard"

Iron Bank is the U.S. Department of Defense’s hardened software repository for critical systems. To be accepted, software must pass some of the strictest security audits in the world. By delivering a Pangeanic-built GenAI engine through Iron Bank, we ensured that:

• All translation operations run in a zero-trust, containerized environment,

• Slang, code words, and even cartel-specific language could be processed securely for law enforcement,

• No data ever leaves the certified environment, ensuring privacy and auditability.

This was about meeting mission-critical requirements where lives, security, and confidentiality are at stake.

The different path: Pangeanic’s Iron Bank Security for Veritone

When Veritone, a NASDAQ-listed AI leader in government and law enforcement, needed a translation engine capable of handling cartel slang, coded criminal expressions, and high-stakes interrogations, they didn’t turn to a generic platform. They partnered with Pangeanic. They needed a task-specific small language model, an AI that could run independently and within their own system. 

Our AI Lab delivered a Machine Translation engine deployed inside Iron Bank — the U.S. Department of Defense’s secure software repository.  This project proved a principle: true enterprise AI isn’t about doing everything — it’s about doing the right things, the right tasks, securely.

An AI fortress, not a public library 

"True enterprise-grade AI requires an enterprise-grade security posture. This is where a private, specialized solution becomes essential. Pangeanic's Iron Bank Security, integrated within the Veritone aiWARE platform, offers a fundamentally different approach. We believe your Generative AI solution should be a secure fortress, not a public library." - Manuel Herranz, CEO

The Iron Bank provides a fully tailored private, secure Generative AI translation ecosystem. And it creates an impenetrable defense for your data:

1. Private Deployment: Your AI models run on-premise or in your own private cloud. This is the ultimate security measure. Your data never leaves your controlled environment. You hold the keys to the vault.

2. Defense-Grade Architecture: Built for Iron Bank, the system operates in a zero-trust, containerized environment. Data never leaves government-controlled infrastructure — a world apart from consumer-grade AI clouds.
   
   

3. Absolute Data Sovereignty: With the Iron Bank, you retain 100% ownership and control over your data. We don't use it to train our models. Your proprietary information is used exclusively to benefit your business, allowing you to fine-tune models for your specific needs without risk.

4. Domain-Specific Intelligence: Pangeanic is a specialist in secure language processing and AI. We aren't distracted by building search engines or social media platforms. Our sole focus is on providing robust, reliable, and secure AI solutions that meet the stringent demands of enterprise, legal, and government clients.

5. Airtight Compliance: By keeping your data in-house, you can easily audit, control, and manage it to meet any regulatory requirement, giving your compliance officers peace of mind.

6. Real-Time Security Operations: Officers could instantly understand conversations during crowd monitoring and interrogations, where seconds mattered. Generic models simply aren’t built for this kind of mission-critical use.
   
   
7. End-to-End Privacy: No data leakage. No hidden training reuse. Full auditability — because in law enforcement and government, trust is non-negotiable.

The "Do-It-All" dilemma:  A jack of all trades, a master of none

Large AI providers offer a vast menu of services, but this breadth often comes at the cost of depth, especially concerning security. Their business model is frequently built on data acquisition. The prompts you enter and the information you upload become fuel for their ever-growing models.

Customization issues aside (you have to change your system and organization to fit in the new token economy), this creates several fundamental problems for any serious enterprise:

• Loss of Control: Your data is no longer yours. It's co-mingled in a massive, opaque system outside of your control and your security perimeter.

• Security Vulnerabilities: A centralized, public-facing model is a prime target for cyberattacks. A single breach could expose the data of thousands of companies.

• Compliance Nightmares: Regulations like GDPR, CCPA, and HIPAA demand strict data governance. Using a public AI service can make it nearly impossible to guarantee compliance and avoid hefty fines.

• Generic Outputs: Models trained on the entire internet are not specialists in your business. They lack the nuanced understanding of your specific terminology, context, and needs.

• Explainability: staff don't know where and how the information comes from (in this case, it was a translation of very specific slang and conversations). In general, staff are unclear about why external AI systems provide the answers they do (the famous hallucination issues).

The Fortress: Pangeanic's Iron Bank Security for Veritone

True enterprise-grade AI requires an enterprise-grade security posture. This is where a private, specialized solution becomes essential. Pangeanic's Iron Bank Security, integrated within the Veritone aiWARE platform, offers a fundamentally different approach. We believe your Generative AI solution should be a secure fortress, not a public library.

The Iron Bank provides a private, secure Generative AI ecosystem tailored for your organization. Here’s how it creates an impenetrable defense for your data:

1. Private Deployment: Your AI models run on-premise or in your own private cloud. This is the ultimate security measure. Your data never leaves your controlled environment. You hold the keys to the vault.

2. Absolute Data Sovereignty: With the Iron Bank, you retain 100% ownership and control over your data. We don't use it to train our models. Your proprietary information is used exclusively to benefit your business, allowing you to fine-tune models for your specific needs without risk.

3. Specialized Expertise: Pangeanic is a specialist in secure language processing and AI. We aren't distracted by building search engines or social media platforms. Our sole focus is providing powerful, reliable, and secure AI solutions that meet the stringent demands of enterprise, legal, and government clients.

4. Airtight Compliance: By keeping your data in-house, you can easily audit, control, and manage it to meet any regulatory requirement, giving your compliance officers peace of mind.

Gartner: private, secure GenAI will beat “AI That Does Everything”

While Big Tech pursues AGI headlines, Gartner predicts a very different reality for enterprises. By 2027/28, organizations will use small, task-specific AI models three times more than general-purpose LLMs. In other words, 80% of AI use will come from specialized systems that are private, secure, and task-specific, adapted to real business needs.

This aligns exactly with Pangeanic’s philosophy. As we outlined in The AI Consolidation is Coming, the future isn’t about building ever-bigger LLMs. It’s about agentic workflows and domain-specific AI that solve problems securely inside the enterprise.

Our recognition in Gartner’s Hype Cycles and Emerging Tech Reports confirms it: the industry now validates what Pangeanic has been doing for years — practical, specialized AI that enterprises can trust.

Key Takeaways

As governments, enterprises, and communities adopt GenAI, the stakes will only grow higher. The real competitive edge will belong to those who can trust their AI systems as much as they trust their legal or financial advisors.

At Pangeanic, we are proud to stand for this principle. Security is not an afterthought — it is the foundation. Our work with Veritone inside Iron Bank proves that GenAI can be both powerful and private.

Because in the end, true intelligence is not about doing everything — it’s about doing the right things securely.

Ready to secure your AI transformation?

Don’t build your AI future in a public library. Build it in a fortress.”

Contact us today to learn how Pangeanic can provide the private, powerful translation, anonymization, or AI solution your enterprise deserves.