Ordinary AI can be useful for many things, but when it comes to business, it’s a liability. Enterprises need AI that is secure and built to protect their sensitive information.

As AI security challenges grow, business leaders are prioritizing privacy and security like never before. Yet, according to Gartner, fewer than half of CIOs adopting AI are confident that their organization can effectively manage its risks.

We launched our Secure AI Frontier Model Framework to show how we engineer secure AI solutions that solve the real-world challenges that businesses and governments face daily.

As industries like finance, healthcare, and critical infrastructure double down on AI, data privacy and security aren’t just priorities — they’re non-negotiables. Our approach is purpose-built for the unique needs of business, delivering AI that’s both powerful and uncompromisingly secure.

In the news

“A lot of the focus we have is on how do you safely and effectively leverage the knowledge and the insights that we have in the firm. Data privacy is our first and foremost priority.” – Teresa Heitsenrether, CDAO, JPMorgan

Secure enterprise AI is becoming imperative

Secure AI refers to systems that protect sensitive information and ensure that data is handled confidentially and securely throughout the AI solution's lifecycle.

It’s becoming an essential consideration at a time when companies are accelerating their adoption of AI, moving from pilot projects to real-world production — with real data and systems at risk. The average cost of a single data breach jumped 10% to $4.9 million last year, underlining the high financial and reputational stakes of feeding sensitive data into AI models and applications.

Industry pulse

$7 billion

This is the estimated cost for U.S. data breaches in the healthcare sector. In 2024, there were 725 data breaches in the healthcare industry, according to the HIPAA Journal.

Sensitive information can leak out through models exposed to third parties, or security threats can sneak in through unsafe code or data.

One study released late last year found that 60% of S&P 500 companies believe they have “material risks” around AI, including cybersecurity, regulatory, and reputational issues.

Security threats grow with AI

As the technology behind AI becomes more sophisticated and the number of providers grows, so do the potential security threats.

For example, you’ve heard of prompts … but have you heard of their more malicious cousin, Imprompter? Researchers recently unveiled this new cyber attack method that allows hackers to secretly instruct an AI model to collect names, IDs, credit card details, and other personal information. It works by using an algorithm that embeds a normal language prompt with a nefarious set of instructions controlled by a hacker.

Fortunately, there doesn’t always have to be a trade-off between performance and security. Our AI security guide offers a primer on the security issues facing enterprises, and how to deal with them effectively.

In the news

"Recent studies show that over 70% of CIOs state that privacy is their number one concern with generative AI. They don't want proprietary company data exfiltrated to third parties.” – Peter Guagenti, CEO, Integrail and former CMO, Tabnine.

The case for private deployment

A private AI deployment is the single most effective guardrail. At a stroke, it cuts out major security threats from AI by creating an air gap between an organization’s data and the outside world. By keeping data on-premises or in a private cloud, companies can better comply with regulations and maintain data sovereignty, avoiding legal and security risks associated with public cloud solutions.

There’s another big security advantage to private deployments — they allow organizations to implement their own security measures and protocols, minimizing attack surfaces and vulnerabilities. This allows companies to build customized models for specific business needs with greater confidence.

In the news

“We’re going to be able to deploy GenAI securely on RBC infrastructure, on our data, rather than relying on the cloud.” – Dr. Foteini Agrafioti, SVP Data & AI and Chief Science Officer, Royal Bank of Canada

Our recently launched North is an AI workspace that empowers employees to improve and accelerate their tasks within a private, air-gapped environment. It combines LLMs, intelligent search, and agents into an intuitive platform that unlocks productivity without sacrificing security or privacy.

We understand that every organization has different needs when it comes to balancing security, privacy, speed, cost, and dozens of other variables. From deploying GenAI on-prem to virtual private cloud and other options combining security with performance, we’ve already done much of the heavy lifting.

Mastering private AI deployments

Is your business ready for a secure, private AI deployment?

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Secure AI that’s good for business and won’t slow you down