Industry Insights with Cameron Hyde , Michael Sanders

The Future of AI & Cybersecurity

Protecting AI Competitive Advantage: From Development to Deployment

Protecting AI Competitive Advantage: From Development to Deployment

Artificial Intelligence (AI) is reshaping industries, enabling faster decision-making, and driving operational efficiency—giving organizations a new path to competitive advantages. However, as AI deployments become central to business operations, they bring unique security challenges that can compromise data integrity and expose sensitive information. From initial development to deployment, protecting AI projects is essential to secure both business value and competitive edge.

See Also: How Active Directory Security Drives Operational Resilience

Let’s explore some risks in AI development and understand how a cloud-native application protection platform (CNAPP) offers robust protection throughout the AI deployment lifecycle.

Vulnerabilities in AI Development: Data Exposure and Supply Chain Risks

AI models rely on vast amounts of data, much of it sensitive or proprietary. As attackers increasingly target AI systems, this dependency creates significant risks of data exposure. Data poisoning attacks, for example, involve injecting manipulated data into training sets, which can cause models to produce biased predictions or hallucinations. Prompt injection and model inversion attacks can be used to allow attackers to extract insights from private data. These risks are especially serious in industries like finance and healthcare, where data confidentiality is paramount.

A critical vulnerability in AI development is supply chain risk from open-source dependencies. Many AI projects leverage open-source models from platforms like Hugging Face to accelerate development. While these resources are valuable, they introduce potential risks. If a dependency in the supply chain is compromised, it can introduce vulnerabilities, allowing attackers to inject malicious code or access sensitive data. Attackers could use such dependencies to create backdoors, exfiltrate sensitive information, or manipulate model outputs—undermining the AI system. Keeping track of these dependencies, along with any patches or security updates, requires significant effort.

Mitigating AI Development Risks with a CNAPP

Securing the entire AI lifecycle from code to cloud requires a comprehensive approach that only Palo Alto Networks Prisma® Cloud can provide. The Prisma Cloud platform offers end-to-end AI security by monitoring application development, deployments, and data dependencies. Prisma Cloud helps security teams quickly detect potential vulnerabilities, such as data leaks or unusual behaviors that are indicative of attacks like data poisoning or model inversion. Prisma Cloud can automatically detect and respond to threats early, before they reach production environments. With Prisma Cloud, you can gain insight into attack paths, mitigate vulnerabilities and misconfigurations, and block active attacks in real time.

Secure Dependency Management and Supply Chain Protection

To reduce the risk of supply chain attacks from open-source packages, Prisma Cloud’s AppSec capabilities ensure third-party libraries and packages meet stringent security standards. The platform scans for known vulnerabilities, assesses compliance, and enforces secure configurations on open-source AI and ML packages. By restricting dependencies to vetted, trusted sources, Prisma Cloud ensures that only secure, reliable libraries are used in AI projects.

Prisma Cloud also monitors updates to third-party dependencies, allowing development teams to apply critical security patches or switch to secure alternatives when needed—all from within the platform. This proactive approach limits the risk of introducing malicious code through supply chain vulnerabilities, preserving the security of AI deployments.

Protecting the Underlying Data in AI Models

Prisma Cloud’s Data Security Posture Management (DSPM) provides visibility into the underlying training and inference data that goes into fine-tuned and Retrieval Augmented Generation (RAG) AI deployments. Understanding the sensitive data that AI models rely on and what guardrails and configurations are in place allows an organization to mitigate risks and minimize attack exposure.

As organizations rely on cloud service providers (CSPs) to host AI deployments, it’s crucial to understand what models are in use as well as the architecture and configuration of the model deployment. Each CSP provides different capabilities and configurations that can cause unnecessary exposure if left unchecked. By having a CNAPP that supports AI environments, security teams can catch misconfigurations such as model exposure, prompt and output handling, content filtering, and jailbreak protections prior to deployment.

Preserving Competitive Advantage with Robust AI Security

As organizations innovate with AI, Prisma Cloud ensures that threats are proactively mitigated, allowing you to leverage the full potential of AI without compromising security or integrity. By integrating security into every phase of AI development, you can confidently deploy AI solutions that are resilient, compliant, and aligned with your competitive goals.

Learn more about how Palo Alto Networks can secure your AI deployments by requesting a free AI-SPM risk assessment.



About the Author

Cameron Hyde

Cameron Hyde

Product Marketing Manager, Prisma Cloud, Palo Alto Networks

Michael Sanders

Michael Sanders

Cloud Security Architect, Palo Alto Networks

Michael Sanders is an accomplished Cloud Security Architect for Palo Alto Networks. With a growing focus on AI security, he is well-versed in addressing AI-specific threats and safeguarding AI models from emerging risks.




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