Artificial intelligence (AI) and machine learning (ML) are finding their way into industries worldwide as organisations look to leverage these technologies to drive innovation, efficiency, and competitive advantage. From healthcare and finance to manufacturing and retail, the potential applications of AI and ML are wide-ranging. However, amidst the growing adoption, it’s worth considering that there are new security challenges that come with implementing these technologies, and that these should ideally be addressed in advance of widespread usage.

The Current AI Landscape, Data Sensitivity and Ad Hoc Adoption

The integration of AI and ML into business processes often involves the use of sensitive corporate data, which can include everything from customer information and financial records to intellectual property and strategic insights. Furthermore, recent research by Freeform Dynamics indicates that AI adoption is expected to take place through a variety of approaches, including embedded AI features within existing ISV solutions. The diverse range of adoption strategies points to the growing use of AI across industries and the potential benefit of putting comprehensive security measures in place ready to address the risks associated with each approach.

However, the adoption of AI and ML is often not limited to ‘official’ IT projects. It can be the case that individual users look to implement these technologies as they try to streamline their workflows, generate original content, and/or acquire new insights. This ad hoc approach can lead to security blind spots as organisations may lack full visibility into where and how AI is being used and what data is being fed into these systems. At the same time the users concerned may be unaware of any limitations of such systems, what it’s legitimate/advisable to use them for, and the safeguards needed to prevent security and compliance issues. 

Pushing existing security measures beyond their limits

One of the primary challenges is that existing security tools, procedures and protocols may not be able to handle AI-specific risks and threats. Traditional security measures, such as firewalls and intrusion detection systems, may not provide adequate protection against AI-related vulnerabilities. Additionally, the complex and often opaque nature of AI and ML systems can make it difficult to understand how data is being used and combined, potentially exposing the organisation to risks not previously encountered or considered.

For example, a marketing department might integrate customer data from various sources into an AI analytics platform in order to gain insights into consumer behaviour. However, if this data is not properly secured and governed, it could be vulnerable to breaches or misuse downstream, e.g. after it has been pulled into an AI system. While AI marketing makes it sometimes seem like magic, it’s not immune to the very real threats of data breaches and misuse. Similarly, an AI-based fraud detection system in a financial institution might inadvertently perpetuate biases if the underlying data is not carefully curated and monitored.

Laying the right foundations

Against the above backdrop, adopting a passive or reactive approach to AI related security will almost guarantee you’ll run into costly and disruptive problems, up to and including reputational damage and compliance exposure. It’s therefore necessary to lay the foundations for effective AI security proactively and as early as you can. 

To address this, we’ve put together 5 suggested steps below to help set you on the path towards a robust AI security strategy. These aren’t intended to be definitive or exhaustive, and we appreciate that you may already have made a good start in some of these areas. However, we speak with so many organisations that are struggling to define what’s important and why, or simply haven’t haven’t had time to think things through, so we thought it was worth touching on all of these essential points:

1. Conduct a Comprehensive AI Risk Assessment: Begin by evaluating your organisation’s current and planned AI activities in as much detail as possible. Identify the types of AI systems and company data being used, the potential vulnerabilities, and the possible consequences of a security breach. Try to make sure you cover both official IT projects, ad hoc AI adoption by individual users, and AI capabilities embedded in business applications and collaboration systems. Engage with stakeholders to form a business view of activity, plans and potential risks.
2. Develop AI-Specific Security Policies and Guidelines: Based on the findings of your adoption roadmap and associated risk assessment, identify and fill gaps in your existing policies and guidelines. As part of this, make sure you take into account requirements for data governance, access controls, model training and testing, and ongoing monitoring and auditing as they relate to AI. Clearly define roles and responsibilities for managing AI security, and ensure that all relevant personnel are trained on these policies.
3. Implement Robust Data Governance: Given the critical role of data in AI and ML systems, strong data governance is essential. Establish clear protocols for data collection, storage, and access, ensuring that sensitive information is properly secured and only accessible to authorised personnel. Regularly review and update these protocols to keep pace with evolving AI use cases and data requirements. The fundamentals here are no different to data governance in general, but it’s important to consider them in an AI context.
4. Invest in AI-Focused Security Tools and Technologies: Traditional security measures may not be sufficient to address AI-specific risks. Investigate and invest in security tools and technologies designed specifically for AI and ML environments. This could include solutions for data encryption, anomaly detection, model explainability, and continuous monitoring of AI systems. Stay informed about emerging AI security technologies and best practices to ensure your defences remain up-to-date.
5. Foster a Culture of AI Security Awareness: Engage employees across the organisation in AI security efforts through regular training and awareness programs. Ensure that everyone understands the potential risks associated with AI and ML, as well as their role in maintaining a secure environment. Encourage open communication and reporting of any suspected security issues or incidents related to AI systems. By fostering a culture of shared responsibility and vigilance, you can better protect your organisation against AI-related threats.

While the sequence of these steps reflects a logical order in which to think through the requirements and develop a first-cut security strategy, taking action will more likely take the form of a more parallel and iterative approach. This is especially important given the rapid pace of development in both technology and usage patterns which is likely to continue for the foreseeable future. In line with this, it’s essential to treat your AI security plan as a “living document,” regularly reviewing and updating it to reflect the latest best practices, tools, and usage patterns. 

Ultimately, AI can potentially add value across many parts of the business, and the right security foundations will allow you to act on opportunities quickly, safely and with confidence.