AI Brings New Risks to Data Security. What You Can Do.

AI risks for data security

One thing is true when it comes to cyber threats: The bad guys will continue to do all they can to stay a step ahead of those trying to defend against cyberattacks. We can expect cybercriminals to use all tools at their disposal, including AI. We are now seeing new cases of more sophisticated AI techniques being used in data breaches and other attacks.

These can include the use of AI to create highly personalized emails or other communications in phishing attacks. These also include AI-generated deepfake videos that appear to be from a company’s CEO and instruct employees to take a specific action that could increase the vulnerability. Other examples include AI-enabled ransomware that can learn as it goes to adapt and modify ransomware files over time, making them more difficult to detect.

Just consider these ripped-from-the-headlines examples:

• T-Mobile suffered a data breach where 37 million customer records were stolen using an AI-powered API to gain unauthorized access and expose sensitive information such as customers’ names, contact details, and PINs.
• Hackers used a targeted phishing campaign against Activision (the maker of Call of Duty video games) that were so realistic that one employee took the bait, leading to a data breach.
• Yum Brands was the victim of a ransomware attack that used AI tools to automate decision-making processes for which data to take to increase the likelihood of a ransom payout. Yum Brands had to close nearly 300 stores for several weeks.

Unfortunately, today’s business leaders anticipate that the problem will only get worse. A recent survey found that 85% of cybersecurity leaders believed that their most recent attacks were powered by AI. The same survey found that 46% of respondents believed generative AI will leave businesses more vulnerable to cyber attacks than they were before the days of widespread AI use.

How Can AI Be Used in Cyber Threats?

The increased adoption and use of AI is clearly leading to new data security concerns due to its ability to process vast amounts of information, often from many different sources, at unprecedented speeds. In turn, this capacity raises new risks of unauthorized access to confidential data and the potential misuse sensitive data. This is especially true, given that AI models can be used to identify previously undetected vulnerabilities or extract patterns in personal information.

It can add up to a situation where bad actors have the advantage. For example, cyber criminals might use AI to automate cyberattacks and quickly increase their scale, making it much more difficult for traditional cybersecurity systems to keep up. As described in the Activision example above, AI can make extremely sophisticated – and convincing – deepfake videos and other assets used in phishing campaigns.

All of this represents a significant change in the overall landscape of security threats and makes it difficult for cybersecurity leaders and even company employees to stay a step ahead.

Best Practices to Protect Against AI-Related Data Security Concerns

Companies can best protect themselves against these new AI-related data security concerns by following these best practices.

• Conduct regular security audits: This includes performing routine security assessments and audits on AI systems and existing data-handling processes to identify vulnerabilities and ensure compliance with data protection regulations.
• Implement strong data encryption: Ensure that all sensitive data, both at rest and in transit, is encrypted using advanced encryption protocols to prevent unauthorized access during AI processing.
• Monitor AI systems for unusual behavior: Implement continuous monitoring and anomaly detection to identify any suspicious behavior in AI models, such as potential data poisoning or unintended model drift.
• Adopt robust access controls: Restrict access to sensitive data and AI systems based on user roles, enforcing multi-factor authentication (MFA) and the principle of least privilege (POLP) to limit exposure.
• Ensure data anonymization: Remove or anonymize personally identifiable information (PII) before AI training to minimize the risk of data breaches or privacy violations.
• Stay up to date with regulations: Regularly review and update internal data protection policies to stay in line with evolving laws like Data Privacy Framework, CCPA, and industry standards for AI ethics and data security.

Effectively safeguarding against AI-driven cyber threats requires a proactive and multi-layered approach to security that evolves alongside emerging risks. One solution? Partnering with a data center provider offering managed security services can further strengthen these efforts by ensuring continuous monitoring, advanced threat detection, and tailored security solutions.

How Data Center Partners Can Make the Difference

A data center partner that offers managed security services can play a crucial role in helping companies address AI-related data security concerns. By providing specialized expertise in data protection and system monitoring – as well as specialized security services such as web application firewalls, vulnerability scanning, and multi-factor authentication – data center operators can help make sure the infrastructure supporting AI systems remains secure.

With a comprehensive portfolio of managed security services, data centers real-time monitoring and threat detection, using advanced tools to quickly identify and mitigate vulnerabilities. This can be especially important for AI, where malicious actors may try to exploit weak points in data storage or processing.

Additionally, a managed security partner can help enforce strong encryption protocols, access controls, and ensure that data is stored and transferred securely within the data center. They may offer automated tools that keep track of who is accessing sensitive data and ensure compliance with regulatory frameworks. This is critical as companies navigate the complexity of securing large datasets often used for AI training, especially when those datasets may contain sensitive or personally identifiable information.

Data center partners can also support companies with regular security audits and updates and can proactive approach to detecting potential vulnerabilities or threats in the AI models and data flows. By handling the complexity of security management, they free up internal teams to focus on further security innovation while ensuring that data security and privacy standards are upheld at every stage.