OpenAI’s ‘AI in the Enterprise’ Report: A Must-Read – But One Crucial Piece Is Missing

We are standing at the threshold of one of the most transformative technological shifts in modern enterprise history. AI is no longer on the horizon – it’s here, it’s powerful, and it’s already reshaping the way businesses think about productivity, creativity, and competitive advantage.

OpenAI’s recent report, ‘AI in the Enterprise‘, offers a concise and thoughtful roadmap for leaders seeking to implement AI within their organizations. It explores practical applications, change management strategies, and foundational operating models.

If you’re currently evaluating how AI can be embedded into your enterprise, this is essential reading.

But amid the optimism and operational insight, there is a conspicuous silence – one that technology and security leaders must address with urgency: cybersecurity.

AI Without Security Is a Risk Multiplier

While the report gives excellent advice on responsible usage and internal adoption, it does not go far enough in emphasizing the architectural, policy, and operational changes required to safeguard AI-powered systems.

Deploying AI into a corporate environment doesn’t just introduce new efficiencies. It creates new attack surfaces, introduces complex data flows, and, if mishandled, amplifies existing vulnerabilities. The absence of security-by-design principles in AI adoption planning isn’t just an oversight – it’s a potential liability.

What’s Missing: A Secure AI Operating Model

To truly operationalize AI in a secure and scalable way, organizations must start treating cybersecurity as a first-class citizen in AI deployment, not a bolt-on or afterthought. Here’s what needs to be embedded into the enterprise AI model:

1) Data Governance and Access Control

AI thrives on data, but not all data is equal, and not all should be accessible. A strong data classification framework and identity-driven access controls and audit trails are essential.

2) Model Integrity and Supply Chain Security

Enterprises must protect against model poisoning, prompt injection, and unauthorized fine-tuning. The AI supply chain — including third-party models and datasets — needs the same scrutiny as traditional software dependencies.

3) Shadow AI Detection

Just as shadow IT once created security blind spots, unapproved AI tools and integrations can create policy violations and data leakage risks. Visibility is critical.

4) Policy Frameworks for Responsible Use

OpenAI emphasizes responsible usage, but this needs translation into enforceable policy, covering acceptable use, data handling, output validation, and incident response protocols.

5) AI-Specific Threat Modeling

Traditional threat modeling doesn’t fully account for adversarial ML, prompt-based exploits, or LLM misuse. We need threat models that evolve alongside AI capabilities.

Leading the Secure AI Conversation

The takeaway isn’t to criticize the value of OpenAI’s report – far from it. It’s one of the most accessible and actionable resources available to business leaders. But as we evangelize AI’s benefits, our responsibility as technology leaders is to broaden the conversation.

AI is not just a technical opportunity – it’s a strategic inflection point. And just like Cloud and DevOps before it, its true value will only be realized when it’s built on a foundation of trust, governance, and security.

At Teneo, we help enterprises assess and evolve their infrastructure to unlock the full potential of AI. Schedule a free no-obligation consultation with Teneo today.