How to Secure Artificial Intelligence and Machine Learning Assets

Machine learning (ML) and artificial intelligence (AI) are heavily promoted as potential replacements for human intelligence. Businesses are trying to automate and speed up decision-making with little human error and nearly all nations view AI as essential for maintaining global competition, despite growing concerns about privacy. 

With the value being placed on ML and AI, it’s now more important that ever to develop more AI assets to ensure data security.

Students pursuing a career in software development, cybersecurity, and data analytics will face this challenging question shortly: how can we protect organizations’ artificial intelligence and machine learning assets from hackers?

Investing in a cyber security degree or software development degree, can give students the knowledge essential for understanding how to protect organizations’ proprietary data, customer-facing applications, and cloud infrastructure.

Protecting AI and ML will continue to be critical for organizations. AI and ML could eventually become the core business engines for organizations. If that is the case, protecting organizations’ data integrity will be essential. If hackers manipulate data, organizations can suffer irreversible harm. 

Why do Organizations Need to Secure AI and ML Data?

Using AI and ML behavioral patterns to create assets leads to intellectual property risks for organizations. Organizations need additional cybersecurity protection to secure these assets because traditional security solutions like firewalls, IDS, and endpoint protection provide little protection for AL and ML. 

When trying to safeguard AI and ML assets, organizations can face risks like: 

• Insertion of false data from hackers into AI systems

• Insertion of corrupt AI data into ML engines (which alters the expected output models)

• Complex attacks (including denial-of-service (DOS) against run-time engines and machine-learning models) that make data inaccessible

Protecting organizations from these risks should be a top priority for any organizational security team. But how is that possible?

Secure Enclaves

Secure enclaves might be the answer to security risks associated with AI and ML assets. A secure enclave is a memory protection that leverages hardware encryption and isolation to protect data. This protection ensures that data within the enclave remains inaccessible and unalterable by any external entity, even if the host system is compromised. Cloud computing platforms such as Amazon Web Services and Microsoft Azure possess hardware capabilities that support secure enclaves.

Secure Enclave Benefits

Enclaves can help businesses through increased data storage and usage control, including software management and retirement.

Secure enclaves can also lower the risk of data exposure during AI learning, transport, and storage. This architecture can facilitate AI training with multiple data sets from various parties without compromising data privacy. 

When it comes to healthcare, insurance, and government compliance requirements, secure enclaves may also offer significant advantages. With enclaves, these organizations can have expanded access to data for analysis while maintaining data privacy.

Protecting AI and Machine Learning Algorithms 

For organizations to stay competitive in their respective markets, companies must figure out how to effectively harness the power of AI and ML. While AL and ML have shown great promise in improving customer expertise, developing software, and monetizing data, many global leaders and industry experts caution against the dangers associated with AI and ML.

Hackers, cybercriminals, and terrorists are investing in AI and ML to increase their cyber attack capabilities. For example, these groups can use AI to increase attack velocity or manipulate AI and ML systems.

What is a Decision Boundary Attack?

Organizations use AI and ML to make business decisions and reduce human error in daily operations. The public often scrutinizes these decisions, producing biased choices that can cause consumer and government complaints. Hackers know this and often carry out a decision boundary attack, where they attempt to insert biased samples into data sets to alter decision logic and exploit organizations. 

Organizations must prepare to defend against this new method of attack while continuing to be privacy sensitive. The bar for privacy protection continues to rise as organizations become more dependent on AI and ML. 

Knowledge for Today and in the Future

Artificial intelligence and machine learning continue to develop at a rate that presents concerns for many industries. Yet, even with this dynamic, AI and ML remain essential tools for organizations wanting to optimize their data, respond faster to cyber attacks, and transform their business models.

AI and ML will continue to be at the forefront of digital transformation for years to come. Students investing in a software development degree, cybersecurity degree, or data analytics degree should make themselves aware of AI & ML. Gaining foundational AI & ML knowledge can better prepare students to enter the workforce of the exciting and ever-evolving technology industry.