Originally published in Cybersecurity Dive on May 20, 2024.
AI applications are vast and varied: we’ve seen everything from intelligent customer service chatbots, AI-powered language processing tools, and even bespoke machine learning models for business analytics. But if there is one glaring hole that has emerged as more enterprises utilize AI, it’s the critical issue of security.
A recent Gartner study predicted that, by 2026, more than 80% of enterprises will have used AI APIs or deployed generative AI applications. That is a staggering number and its applications cannot go unregulated. Never has it been more important to ensure the confidentiality and integrity of the data this AI is processing.
The spotlight on the AI era can sometimes seem as if it’s only shining on its benefits. But we’d be remiss not to acknowledge it’s also an era in constant flux and rife with escalating threats. Safeguarding digital assets in the wake of AI and preserving the integrity of networks is a tireless pursuit. And amidst this pursuit, two key challenges consistently emerge: unsanctioned AI usage and ever-evolving, complex AI infrastructures that follow.
Yes, AI is a game-changer. But when the game changes, so, too, do the rules. These challenges, while serious and complex, are solvable with a holistic approach to AI security.
Unsanctioned AI
The proliferation of third-party AI applications coupled with the allure of its benefits often leads employees to bypass official channels and deploy them without proper approval or oversight. And despite the best efforts of IT departments to enforce security policies and guidelines, this unsanctioned usage poses significant security and data risks for organizations. Without visibility into which AI applications are being used, how they're being used, and the associated risk profile, IT departments often find themselves in the dark, unable to effectively monitor and mitigate potential threats.
Third-party AI applications also introduce additional complexities related to data privacy, compliance, and governance. As these applications mature, so, too, should their sophistication. But that’s not always the case, as these applications have inadvertently exposed sensitive data and violated regulatory requirements, heightening the risk of costly data breaches and legal repercussions.
Security is no longer a matter of technical prowess; it's a strategic imperative that demands innovative solutions. Furthermore, to imply there is a single solution to addressing the challenges associated with unsanctioned AI usage would be misleading; it unequivocally requires a multi-faceted approach. Organizations must implement robust policies and procedures for evaluating, approving, and monitoring the use of AI applications. They must also invest in comprehensive AI security solutions that provide visibility into all AI usage across the enterprise, enabling proactive threat detection and response.
Compounding AI Infrastructure
Whether enterprises are dipping their toes into AI or building its uses into their operations, they face the daunting task of managing increasingly complex infrastructures. Protecting AI infrastructure requires organizations to address a seemingly endless number of security concerns, including securing the AI development lifecycle, guarding sensitive data, and defending against AI-driven attacks. With AI models and datasets becoming increasingly valuable assets, organizations must implement resilient security measures to safeguard them from unauthorized access, manipulation, or theft.
What’s more, failure to ensure an organization’s AI operations comply with relevant regulatory requirements, industry standards, and internal policies could result in costly fines, reputational damage, and legal liabilities.
In response to this growing AI usage, organizations have begun to build intricate ecosystems of tools, platforms, and technologies to support AI-powered applications. In fact, according to recent studies, the average company employs a staggering 45 cybersecurity tools. But this rapid expansion hasn’t solved the problem, it’s created a convoluted mess of tools that have led to undue fragmentation and interoperability issues. There is a better way and companies must be proactive in their approach to building a thoughtful infrastructure.
Securing AI Tools
The first step in solving these challenges (and perhaps the most obvious step) is ensuring that confidential data remains secure when using AI tools. While there are a myriad of strategies to safeguard confidential data in the context of AI operations, here are the ones that are most critical.
Data Encryption
Implementing encryption mechanisms can help protect information from unauthorized access. By encrypting data both at rest and in transit, organizations can ensure that confidential data remains secure, even if it falls into the wrong hands. Advanced encryption algorithms and key management practices can further enhance data protection and mitigate the risk of data breaches.
Access Controls
Enforcing strict access controls is essential for limiting access to confidential data and preventing unauthorized users from viewing or modifying sensitive information. Role-based access controls (RBAC), multi-factor authentication (MFA), and privileged access management (PAM) solutions can help organizations enforce least privilege principles and restrict access to sensitive data based on user roles and permissions.
Anonymization and Pseudonymization
Anonymizing or pseudonymizing data can reduce the risk of data exposure and help organizations comply with data privacy regulations such as GDPR and CCPA. By replacing personally identifiable information (PII) with anonymized or pseudonymized identifiers, organizations can protect individual privacy while still deriving valuable insights from the data.
Compliance with Regulations
Compliance with data privacy and security regulations is crucial for organizations leveraging AI tools and technologies. Regulations such as GDPR, HIPAA, and PCI-DSS impose strict requirements for the handling, processing, and storage of confidential data. Organizations must ensure that their AI operations comply with these regulations to avoid legal ramifications and reputational damage.
Regular Audits and Monitoring
Conducting regular audits and monitoring activities can help organizations detect and mitigate potential security risks and compliance issues. By continuously monitoring AI operations, organizations can identify anomalous behavior, unauthorized access attempts, and data breaches in real-time, allowing them to take prompt corrective action and prevent additional damage.
Platformization
But how can companies implement and enact these policies if one of the biggest hurdles of AI security is its complexity? The best thing they can do is to embrace the idea of platformization. Platformization can fundamentally change how enterprises approach AI security by centralizing efforts within existing cybersecurity platforms.
This will enable enterprises to manage AI security alongside other cybersecurity functions, such as network security, endpoint protection, and cloud security. Implementing this centralized approach allows security teams to monitor and mitigate threats more effectively, minimizing the risk of security breaches and data loss.
Securing AI Operations
It’s not just enough, though, that the tools are secure, a company’s AI operations must also be sound. Perhaps one of the most innovative steps a company can employ to secure these operations is to develop custom AI models.
Developing custom AI models is not a simple process, but it’s a necessary one. It involves a series of steps, including data collection, preprocessing, model training, evaluation, and deployment. While venturing across these steps, organizations must also carefully define their business objectives, gather relevant data sources, and select appropriate algorithms and techniques to train the model. Custom AI models are often built using machine learning frameworks, allowing organizations to leverage advanced algorithms and techniques to address specific business challenges.
Even considering all of these techniques, securing AI operations involves more than just developing and deploying custom AI models. Security considerations should be incorporated into every stage of the AI development lifecycle to mitigate potential risks and vulnerabilities. This includes implementing data security measures to protect sensitive information, integrating authentication and authorization mechanisms to control access to AI resources, and conducting thorough security assessments and code reviews to identify and address security flaws and weaknesses.
By incorporating these security considerations and best practices into the AI development lifecycle, organizations can effectively secure their AI operations.
Looking Ahead
Organizations must safeguard and protect their digital assets – this should never be negotiable – because when it comes to AI, the issue is paramount. As more enterprises continue to explore and embrace AI to drive digital transformation, it's essential to stay vigilant and proactive in defense strategies and adopt a holistic approach to cybersecurity.
There needs to be more diligence when it comes to AI applications and more security protocols to protect against unsanctioned usage. These approaches should include integrated security solutions that provide end-to-end visibility, centralized management, and automated threat detection and response capabilities.
By taking necessary and thoughtful steps to secure AI usage and infrastructure, organizations can mitigate risks, protect sensitive data, and ensure the integrity of all AI deployments.