In just a few years, AI has become a part of everyday life for tens of millions of people. Over half of Americans have used an AI assistant, with a third using one every day to assist with tasks ranging from planning meals to organizing trips to writing emails and composing poetry.

Businesses and other organizations, by contrast, have taken a more cautious approach due to concerns such as security, costs, and accuracy. Ironically, though, that slower pace is increasing the very risks they want to contain.

Lacking effective AI solutions at work, many employees are using publicly available tools surreptitiously at work, a phenomenon known as Shadow AI. This is opening a backdoor through which sensitive, proprietary data can leak into the open, raising major reputational and financial risks for organizations.

38% of employees surveyed admitted to sharing sensitive information with AI tools at work.

The chatty assistants that people use at home, and surreptitiously at work, are what you could call Ordinary AI: useful and convenient, but lacking many of the security safeguards and enterprise controls and capabilities that businesses and other large organizations need to keep data safe. With Ordinary AI, it’s uncertain how data is kept safe. Most enterprises need Secure AI — a truly robust, enterprise-grade approach to deploying the technology in a way that prioritizes security, compliance, and trust.

1 in 5 UK companies experienced data leakage because of employees using GenAI according to a new CISO survey.

The best way for organizations to address this growing risk is to accelerate their deployment of Secure AI.

Following are 10 reasons why Secure AI is essential:

Keeps data private
Delivers more reliability
Strengthens compliance
Enhances model and application security
Reduces ROI risks
Enables fast and secure AI customization
Provides greater transparency and trust
Supports safety governance
Accelerates AI deployment and adoption
Focuses on enterprise AI innovation

1. Keeps data private

Secure AI in private deployments ensures that your data stays private — never leaving your secure environment. This is particularly crucial for organizations such as healthcare firms, banks, and governments that process and store a large amount of sensitive data. Just one instance of a leak of private data can do tremendous damage to an organization’s brand reputation, not to mention the financial cost — data breaches cost U.S. healthcare firms an average of $10 million, for example.

Ordinary AI exposes organizations to greater data leak risks. When employees casually copy-and-paste internal emails, source code, or design drafts into a public AI assistant, that data can be stored on someone else’s server or used to further train a model. A recent report by cybersecurity firm Cybsafe found that a whopping 46% of GenZ and 43% of millennials surveyed admitted to sharing sensitive work information with AI without their employer’s knowledge.

One such employee, a software engineer quoted in a recent BBC article, summed up the attitude: "It's easier to get forgiveness than permission. Just get on with it. And if you get in trouble later, then clear it up."

Secure AI fixes this. It cuts off the temptation to use Ordinary AI by putting powerful AI tools at employees’ fingertips. By allowing AI models to be deployed in a private environment — either on-premises or a dedicated private cloud — it ensures that data is sealed off from leaks and remains off-limits to third parties.

“What we're doing at Cohere is to architect all of our products from the models to North to Compass to be completely privately deployable.” – Aidan Gomez, Co-Founder and CEO, Cohere.

2. Delivers more reliability

Even the most advanced “out-of-the-box” general AI models can struggle with consistent accuracy. Because they are trained on broad data rather than industry-specific information and terminology, they may misinterpret terms or miss contextual cues, leading to less predictable, accurate results. Without being grounded on a company’s data, they are more likely to fill in the gaps with guesswork, or hallucinations.

Secure AI enables safer retrieval-augmented generation (RAG) which tightly grounds responses in verified enterprise data, reducing the risk of hallucinations and ensuring outputs stay accurate and context-aware. By controlling the provenance and integrity of the data pipeline, Secure AI helps organizations deploy RAG systems with greater confidence — making AI answers not just more useful, but more trustworthy.

Plus, as AI tools become more integrated into workflows and more autonomous — such as with agentic AI systems — Secure AI can help mitigate potential risks. Before integrating these agents into core systems or decision-making processes, businesses need clear guardrails, rigorous evaluation, and fallback mechanisms to ensure they behave predictably under pressure.

3. Strengthens privacy compliance

Companies globally in many sectors are subject to an expanding alphabet soup of regulations around their use of personal data, from HIPAA to GDPR to CCPA. These rules particularly affect firms that hold large amounts of sensitive, personal data, such as healthcare and financial firms as well as government agencies.

Ordinary AI models typically aren’t designed to meet these rules out of the box, leaving organizations vulnerable to evolving compliance-related and privacy risks. Stanford's 2025 AI Index Report highlights the growing concerns surrounding AI privacy and third-party risks and reveals 80.4% of U.S. local policymakers surveyed support stricter data privacy regulations, which could help tackle the 56.4% increase in AI-related incidents last year. Across all risks, there were 233 reported cases in 2024.

With Secure AI, companies can specify data residency to meet regional or national requirements and build in audit logs to improve accountability and show adherence to rules.

4. Enhances model and application security

Aside from preventing data leaks, Secure AI provides further layers of security that help to safeguard organizations’ systems against malicious actors and unauthorized use.

Off-the-shelf AI models running on the public cloud can become attack vectors — subject to threats such as prompt injection and model manipulation. Enterprise-grade AI running in air-gapped environments dramatically limits this exposure. Secure AI can also be integrated in company cybersecurity stacks and incorporate role-based access to ensure that only authorized users and systems can retrieve certain data.

Secure AI helps organizations have added control over every aspect of their system as it can be sealed off from third parties that could introduce threats. For more guidance on secure deployment of large language models (LLMs), please refer to our AI Security guide on deploying LLMs.

5. Reduces ROI risks

Secure AI solutions enable organizations to scale their AI deployment more efficiently and predictably. Because they are built specifically for business, they give enterprises access to both generative and retrieval systems that are more efficient, secure, and designed to be scalable.

“Our model [Command A] has been designed from the ground up to be enterprise first... specifically designed to do really well on things like speaking languages of global business, operating across internal documents that are private and secure that can be deployed efficiently with a very minimal kind of memory footprint.” - Max Bartolo, Command A technical lead, Cohere

While private deployment can come with higher initial capital outlay and set-up costs, and VPCs incur some resourcing, it can be a smart financial choice over time. Private deployments can provide enhanced future-proofing, robust security, and more granular control to companies adding substantial value over time.

By comparison, public cloud services operate on a pay-per-token basis — costs that can quickly spiral for users with heavy daily inference needs. Self-hosted models give enterprises more predictability and autonomy, making it easier to get a clear ROI from deployments.

For example, they can experiment with a hybrid workload approach — running critical AI functions internally while using cloud-based APIs for less sensitive tasks. While combining more flexibility with security, hybrid solutions come with challenges and costs such as ensuring consistent data governance and controls. Still, some 72% of executives agree that hybrid cloud solutions will be essential for scaling AI and managing the cost of compute.

6. Enables fast and secure AI customization

Ordinary AI might impress with its ability to give life advice or create meal plans, but it isn’t steeped in the knowledge your organization depends on. Secure AI platforms make it easier for enterprises to customize a model on their own data, safely inside their private cloud or on-prem environment. Sensitive data never has to leave your company’s firewall.

In contrast to general-purpose AI, customization gives organizations AI that really knows their business and industry, enabling them to unlock new capabilities, improve core functions, and increase efficiency, all tuned to their unique goals, language, data, and workflows. It also helps to improve the accuracy and relevance of responses, leading to better performance and building employee and customer trust in AI tools.

It allows companies to choose from a range of customization approaches – such as pre-training, post-training, and fine-tuning — according to their unique needs and budget.

“Enterprises want models that understand their domain intimately, not just bigger models.” — Aidan Gomez, Cohere CEO and co-founder

7. Provides greater transparency and trust

Ordinary AI may be convenient, but it’s often a black box in terms of allowing users to see how and why it reached its answers.

Secure AI helps organizations deliver the transparency they need, ensuring that a model’s output is traceable. This not only helps with compliance, it enables organizations to trace incorrect or unusual responses back to its source. With the full control over their AI stack that comes with private deployments, companies can implement their own logging and access controls rather than entrusting this to third parties.

Secure AI is more likely to have a robust citation system built in, adding an important layer of trust, explainability, and transparency. Citations provide a bridge between model responses and the underlying data it’s drawing from, and can be tailored to the level of detail and timeliness needed by different industries.

8. Supports safety governance

Secure AI built for business means safeguards focused on real-world, practical challenges that businesses and governments face today when using AI. All it takes is one rogue AI output to undermine a company’s hard-won reputation. Secure AI aims to proactively reduce the risk of harm caused by both intentional abuse and unintended consequences of how AI systems are used in real-world environments.

Secure AI providers battle-test their models by running red-team exercises, using benchmarking to limit potential responses, and integrating other safety measures. Cohere, for example, allows its customers to choose between different safety modes based on their use cases and business needs. And has a comprehensive security and safety framework that ensures models are designed to holistically manage risks that matter to customers’ unique regulatory, safety, and security needs.

9. Accelerates AI deployment and adoption

AI adoption in complex, regulated industries is being held up by a range of factors, including concern over data privacy, compliance issues, and skills shortage. Some 39% of bank executives say security and data privacy concerns are dragging on the pace of deployment, with 33% highlighting a lack of AI skills.

Deploying Secure AI privately enables organizations to ease such bottlenecks and unlock powerful performance advantages over public, off-the-shelf models.

Cohere’s Secure AI workspace North, for example, comes with pre-built connectors that allow organizations to use AI in a private environment through the apps and tools they use every day. This low-to-no-code approach lets companies focus on fast AI wins without the worry of shadow AI taking hold.

Additionally, since private AI runs where the data and hardware lives, avoiding time-consuming round trips to external APIs, it leads to lower latency and faster inference times. That extra deployment speed can be crucial for many business functions — for example, a bank that needs to detect fraud in real time.

10. Focuses on enterprise AI innovation

Opting for a Secure AI solution usually means working with an expert provider, one that can become a long-term partner in supporting the journey to more advanced AI applications and innovation. These partnerships can help to embed best practices, improve in-house expertise, and build the trust in AI systems needed to accelerate adoption.

Strong partnerships can be vital to clear bottlenecks to AI deployment in regulated industries, where adoption of the technology has been relatively slow compared to other sectors. The healthcare sector is below the global average in AI adoption, with the insurance industry and public sector trailing even further behind, according to a BCG report. And Only 6% of retail banks have a roadmap for enterprise-wide AI transformation.

This underlines how enterprises need a true Secure AI partner, not just an AI supplier.

“Our workshops with Cohere are essentially an acceleration for us to be at the forefront of generative AI-based technology.” — Nicholas Lee, CEO, Fujitsu Intelligence.

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