According to a study by IBM, the average cost of a data breach in 2024 is $4.88 million globally, and a significant driver of these incidents is a failure to adequately secure or manage data across complex, AI-enabled systems. In an era where data breaches can cost enterprises millions and damage their reputations, data security has become a top priority for corporations. The rise of Artificial Intelligence (AI) offers businesses unprecedented capabilities to protect, manage, and utilise their data, but it also introduces new risks in data handling and privacy. 

Private AI emerges as the ideal solution for corporations seeking to balance AI's advantages with data security. Unlike Public AI, which operates on third-party cloud infrastructure, Private AI allows businesses to keep their data within their own network, giving them greater control over data security practices. This article will explore the risks AI brings to corporate data security, why Private AI is a safer choice, how AI can be deployed securely, and the essential role AI plays in protecting data.

Understanding the Cyber Security Risks AI Poses to Corporate Data Security

AI-driven technology can be a double-edged sword when it comes to corporate data security. AI systems are complex and often require vast amounts of data to function effectively. This data collection can expose companies to various vulnerabilities if not managed properly. Public AI tools, like large language models (LLMs) accessible via the cloud, make data transfer simpler but also open more opportunities for breaches. 

Data Over-Collection

One of AI’s greatest strengths is its ability to process vast quantities of data quickly and efficiently. However, it’s often necessary for AI systems to collect more data than would typically be required, which can lead to “data over-collection.” Public AI tools, often trained on data from numerous sources, may be inadvertently exposed to sensitive company data during their processes. In fact, one in five companies in the UK has had potentially sensitive corporate data exposed through the use of Generative AI. 

Lack of Transparency

Many AI models operate as “black boxes,” meaning their internal workings are opaque and hard to interpret. This lack of transparency makes it challenging for companies to understand how their data is processed, stored, and used, leaving them vulnerable to potential mishandling of sensitive data.

External Threats

AI is not only vulnerable in how it handles data but also in how easily it can be manipulated by external threats, contributing to various cyber security risks. Hackers can introduce subtle changes to input data, leading the AI to make incorrect decisions, exposing sensitive data or weakening security postures. For instance, adversarial attacks can make an AI model incorrectly classify data, which could cause a lapse in security checks. Data poisoning attacks, where malicious actors alter AI training data, can also undermine data security by disrupting an AI’s learning process, leaving sensitive information exposed to attacks.

While these risks affect both Private and Public AI implementations, they are more challenging to control within Public AI. Public AI infrastructure, often shared across clients, introduces additional risks in terms of data transfer and exposure, creating an increased risk of external access or third-party vulnerabilities. 

As companies look to leverage AI while protecting their valuable data, they must carefully consider how to implement AI securely—which is where Private AI offers distinct advantages.

Managing the Risks: Why Private AI is the Safer Option

When it comes to mitigating the risks posed by AI, Private AI stands out as the safer option for corporations managing sensitive data. To understand why, it’s essential to define the key distinctions between Private AI and Public AI:

• Private AI refers to AI systems hosted on an organisation’s internal infrastructure. By keeping AI infrastructure and data on internal servers, Private AI provides better control over data handling and processing.
• Public AI involves outsourcing AI functions to third-party cloud providers. While this approach can be cost-effective and fast to deploy, it means that a company’s sensitive data is processed and stored on external servers, which limits control over security practices and increases potential vulnerabilities.

Advantages of Private AI for Corporate Data Security:

• Enhanced Data Control: With Private AI, data remains within a company’s own network, reducing exposure to external threats. This is particularly important for industries where data protection and compliance are essential, such as healthcare and finance. Internal storage and processing mean that the company, not a third party, is in full control of its data security practices. This added control significantly reduces the likelihood of a data breach, making private AI an increasingly popular choice for businesses with stringent security needs.
• Customisability: Unlike Public AI systems that follow standardised models, Private AI can be customised to adhere to a company’s unique security policies. This allows for integration with other internal security measures and enhances the organisation’s ability to comply with regulatory and security requirements.

Public AI, on the other hand, may compromise data security due to shared infrastructure risks and limited flexibility. As companies increasingly recognize the importance of data sovereignty, Private AI emerges as the preferred solution for handling sensitive data securely. For businesses with a need to prioritise data security, Clairo AI's private agents are a secure solution, allowing them to securely connect their enterprise data in a private environment.

Given these distinct benefits of Private AI, what steps can organisations take to further secure their AI deployments and protect their data effectively?

Implementing AI Safely: A Strategic Approach to Secure Data Handling

The key to reaping the benefits of AI without compromising data security lies in a strategic approach to secure data handling. For corporations deploying AI, this involves following robust security practices, regardless of whether they are using Private or Public AI, though these practices are often easier to enforce with Private AI.

Key Strategies for Secure AI Integration:

Data Access Management

Restricting access to sensitive data is essential. Implementing role-based access controls (RBAC) ensures that only authorised personnel and applications can access certain data sets. This granular approach minimises the risk of unauthorised data exposure and enforces data access policies aligned with security requirements.

Anonymisation and Encryption

Sensitive data can be protected by anonymizing identifiable information before it is processed by AI models. Encryption further secures data both at rest and in transit, ensuring that even if data is intercepted, it remains unreadable to unauthorised users. For Private AI, maintaining encryption keys in-house provides an added layer of control.

Using Private AI for Enhanced Customizability and Control

Private AI solutions, such as those offered by Clairo AI, allow businesses to customise and enforce advanced security protocols within their own infrastructure. This flexibility enables companies to address their unique security needs effectively, while retaining full control over data handling and processing methods.

Incident Response and Preparedness

An effective incident response plan for AI-related data breaches can minimise potential damage. Companies should establish rapid response workflows to manage incidents, including data isolation, containment, and recovery measures. Regular testing and updates to the response plan ensure the organisation remains prepared for emerging threats.

Private AI enables a company to implement these security measures more effectively, providing an environment where AI systems align with the specific security needs of the organisation. Public AI, in contrast, often limits flexibility in security practices due to reliance on external infrastructure.

Beyond the strategies that enhance data security, AI itself can be a valuable ally in actively managing and mitigating cybersecurity threats.

How AI Enhances Data Security: AI’s Role in Managing Cyber Threats

When implemented with a focus on security, AI can play an active role in enhancing data protection. The same study by IBM found that companies that used AI to support security prevention measures saw a substantial impact in reducing a data breach, saving an average of $2.22 million, compared to companies that did not use AI in this way. Private AI systems, in particular, can be instrumental in managing cyber threats due to their secure and customizable nature.

• Threat Detection and Anomaly Analysis: Private AI models can monitor network activity and analyse patterns to detect unusual behaviour, helping to identify potential breaches early. With data securely housed in a private network, AI can operate in real-time to flag and investigate anomalies. 
• Automated Incident Response: In cybersecurity, rapid response is critical for minimising potential damage, and AI-powered incident response agents can react immediately, assessing and mitigating risks before they escalate. AI can automate repetitive tasks such as isolating affected systems or blocking malicious traffic, reducing the workload on human security teams and enabling faster, more efficient response times.
• Vulnerability Management: AI can proactively scan systems to detect vulnerabilities, enabling the company to address security weaknesses before they are exploited. By integrating these capabilities within a Private AI framework, businesses retain full control over vulnerability assessment processes.

When deployed responsibly, AI strengthens data security by automating threat management and supporting proactive defence mechanisms. Incorporating private AI agents for cybersecurity offers an additional layer of protection. These specialised agents operate within a company’s secure environment, ensuring that data remains confidential and protected.

While AI can help protect against data breaches, it's important to remember that AI itself poses risks. The same systems designed to secure data can be vulnerable to attacks if not properly managed. It's not just about using AI to defend against threats; how you implement and control AI is just as important in ensuring data security. Clairo AI can help companies refine their approach by providing tailored solutions that ensure data is handled securely, optimising outcomes while maintaining full control over sensitive information.

Conclusion: Private AI as the Preferred Choice for Secure Data Management

In today’s evolving digital landscape, AI is a powerful asset for companies seeking to protect their data, but it also introduces new security risks.

Private AI provides control, customisability, and data security for businesses. By keeping AI infrastructure in-house, businesses can protect sensitive data from external threats and ensure compliance with data protection standards. When implemented carefully, AI becomes a valuable tool in managing cyber threats, fortifying a company’s defences, and supporting long-term data security.

For companies committed to data protection, Private AI represents the safest and most effective path forward, combining the benefits of AI with robust security practices. By choosing Clairo AI, companies can enjoy the benefits of innovative AI-driven insights while ensuring that their data remains securely managed within a private, compliant infrastructure.