As GenAI and machine learning (ML) become more widespread across industries, their high levels of adoption have created a major challenge: security. While every organization and IT team has its own security protocols and frameworks, many are starting to realize that traditional approaches aren’t enough when it comes to protecting themselves from the potential threats of artificial intelligence (AI) and machine learning systems.

This becomes even more problematic when you realize how critical ML models and systems have become to organizations across the globe. With so many organizations adopting GenAI, a fast-growing subset of ML, the desire to deploy quickly creates a risk of not implementing a proper level of security.

The growing use of AI throughout the organization naturally expands a company’s attack surface. Let’s explore the different elements that make ML models and components vulnerable, and what organizations can do to protect themselves.

The Expanding Attack Surface Caused by Use of ML Systems

ML models are high-value attack targets for bad actors for several reasons:

• High economic value: Companies rely on ML models to boost efficiency, create competitive advantages and generate revenue. Businesses from manufacturing to finance and beyond have deployed ML for anomaly detection, to improve customer relations and to automate time-consuming tasks, and they’ve become a core component of operations for many businesses.
• Business-critical decisions: Whether it’s quickly extracting insights, predicting potential outcomes based on historical data, or identifying key trends across vast amounts of data, ML supports crucial functions such as fraud detection, risk assessment, and medical imaging.
• Deep integration: With ML becoming more widespread, models regularly interact with an organization’s sensitive data and overarching infrastructure.
• Explosive growth: As is often the case when new technologies are introduced and experience rapid growth, the adoption of ML is outpacing security awareness and implementation, creating gaps that attackers can exploit.

With ML driving core business functions, it has fundamentally altered the software development lifecycle (SDLC). Organizations now depend on models, model dependencies, and datasets as part of their supply chain, introducing new risks and cyber threats that traditional security frameworks typically don’t address.

The bottom line: We’ve entered a new age of software product development, and malicious actors are aware.

Why Is Machine Learning So Vulnerable?

Machine learning remains susceptible to malicious threats because, unlike traditional software, machine learning models contain attack vectors that require unique security measures. The characteristics of these models, including complex behavior and voluminous datasets, combined with teams’ increased reliance on automated pipelines, make it hard to detect suspicious activities and mitigate threats efficiently.

Four key factors make ML particularly susceptible to attacks:

1. Low security awareness: Simply put, far too many stakeholders overlook ML-specific security risks.
2. Models are attack vectors: Compromised models can execute arbitrary code, leading to data leaks or compromised systems.
3. Weak detection mechanisms: There is a shortage of tools that verify a model’s integrity, making it difficult to detect meddling or model manipulation.
4. MLOps weaknesses: Immature ML platforms allow attackers to move laterally within systems, creating the potential for security breaches in broader enterprise environments.

Plan Your Work and Work Your Plan: Four Steps to Safeguarding ML Systems

Securing machine learning models, data and infrastructure has become a top priority for security teams as ML systems introduce new security challenges. Addressing these risks calls for a proactive and security-first approach throughout the ML lifecycle, which has become common for DevSecOps teams in traditional software development. Each organization has its unique needs and standards, but these six strategies help create a foundation of success in protecting ML models from future attacks:

1. Treat ML as part of the software supply chain. The same security best practices used in traditional software supply chains should be applied to ML. This includes implementing controls to enhance your security posture for model dependencies, conducting regular checks for potential vulnerabilities in ML components, and creating a security-first culture across development teams.

2. Gain and maintain full visibility. You can’t protect what you can’t see, so full and real-time visibility across models, datasets, configurations and parameters is vital. Teams also need easy access to metrics that gauge and influence model performance, along with full knowledge of the security risks associated with each ML component.

3. Enforce strict governance. Policy-based access controls prevent unauthorized modifications to models, and security teams should monitor for abnormal or potentially malicious activities, such as unauthorized updates or data exfiltration. Organizations should also implement secure environments to train, test, and deploy models.

4. Secure your ML from day one. Embedding security throughout the lifecycle has become a software development best practice, and it’s no different with ML. This may include security audits during model development and validation and using secure data pipelines to limit these types of attacks. The importance of enforcing strict validation protocols for model inputs and outputs can’t be overstated, and security measures should be incorporated into model curation and package management.

As GenAI and ML increasingly become mainstream, so will the cyber threats targeting these AI-driven systems. Organizations are faced with a mandate: recognize ML as a critical asset and implement appropriate security or risk being compromised. It’s that simple.

By integrating security into every phase of the ML software development lifecycle, businesses give themselves the best chance at truly fortifying their systems and staying ahead of today’s rapidly evolving and increasingly sophisticated threat landscape.