Do You Really Know Your AI Landscape?

Enterprise adoption of AI is no longer a future trend; it's a present-day reality. As organizations race to leverage AI for innovations, security teams are grappling with a new, complex, and dynamic attack surface. AI is breaking the operational silos that currently segregate Cloud, SaaS and Endpoint Security; AI is everywhere and it is consuming enterprise data and assets across these channels. Traditional security tools, designed for cloud infrastructure and SaaS applications, are fundamentally ill-equipped to handle the unique risks posed by AI.

AI security posture management (AI-SPM) solutions can provide relief by protecting critical AI assets, but it's important to note that not all AI-SPM solutions are created equal. Many solutions offer only basic posture checks and are focused predominantly on infrastructure and vulnerability management. In addition, most focus solely on Cloud or SaaS, leaving many blind spots when trying to get the full picture of your AI landscape.

Key security challenges are creating demand for advanced AI security posture management

Basic AI-SPM might identify AI models, services, and data, but it tends to stop there. Security teams need deeper insights as AI applications are not monolithic entities but rather a complex assembly of models, datasets, identities, code dependencies, and APIs.

In order to truly address AI risk, we need to fundamentally understand this ecosystem and be able to answer the following questions:

• Which models are in use, both sanctioned (managed) and unsanctioned (unmanaged)?
• What are the inherent risks associated with the models?
• Where are my AI agents, how are they interacting with the models?
• What identities are being leveraged by AI?
• Where are my AI orchestration tools and model context protocol (MCP) servers?
• Which datasets are these models trained on?
• Can we prove data lineage for compliance?
• What is my AI supply chain risk, where are the models coming from, what's the risk of using pytorch models?

Let's take a close look at some of the top AI risks and security challenges security teams are facing today:

How to secure the AI supply chain—Identifying and mitigating supply chain risks in AI

The AI supply chain is a complex web of dependencies that attackers are actively targeting. With supply chain breaches costing nearly $4.5 million on average, organizations cannot afford to ignore the risks embedded in their AI models and libraries.

Key supply chain risks include:

• Missing model provenance: Without a model's "birth certificate"—a clear record of its origin, training data, and history—security teams cannot verify its integrity or ensure it's free from malicious backdoors.
• Vulnerable dependencies: Modern AI development relies on third-party models, from hubs like Hugging Face and open source AI libraries. Each external component is a potential entry point for attackers, allowing a single compromised library to undermine an entire organization's AI posture.

Mitigating these risks requires integrating deep AI supply chain visibility and validation into your core security program.

Understanding and preventing common AI model vulnerabilities

AI models are subjected to unique and rapidly evolving vulnerabilities that attackers can exploit across the entire AI lifecycle—from development to deployment and operation. These risks go beyond traditional security risk and can result in data breaches, intellectual property theft, and operational disruptions. Key AI model vulnerability risks include:

• Direct model vulnerabilities: Threat actors are actively exploiting vulnerabilities backdoored ML models by planting executable and malicious code inside Python based serialisable models such as pytorch and Kerras based models.
• Dataset and training vulnerabilities: The security of a model is contingent on the security of its training data. Data poisoning attacks can subtly corrupt a training dataset to create specific, exploitable behaviors in the deployed model. Biased or non-compliant data introduces significant reputational and regulatory risk.
• Shadow AI vulnerabilities: "Shadow AI" or unmanaged models are those used by developers and data scientists without security oversight. These models are deployed into containers and workloads that are not easily visible to the Cloud infrastructure. These unmanaged assets are often sourced from untrusted locations and operate without any security controls, creating massive blind spots.

Model context protocol (MCP): Security risks and how to protect enterprise AI integrations

Model context protocol (MCP) connects AI models directly to live enterprise systems, creating a powerful but high-risk integration layer invisible to traditional security. A compromised MCP server is a master key to your data and APIs. Developers are adding MCP servers and capabilities to almost every Enterprise application without security oversight.

Key risks include:

• Massive blast radius: As a new protocol linking disparate systems, a single compromised MCP server can disrupt operations enterprise-wide.
• Centralized credential risk: MCP servers act as a vault for access tokens. A breach grants attackers widespread lateral access to countless connected services.
• Tool poisoning: Attackers can embed malicious commands in tool metadata, tricking an LLM into executing unauthorized actions like data exfiltration.
• Implementation flaws: Poorly coded MCP servers are vulnerable to classic exploits like command injection, creating pathways for privilege escalation and lateral movement.

Securing MCP requires a new class of security capable of monitoring and enforcing policy on this unique protocol.

Data lineage: The missing link in AI security

Data lineage is the cornerstone of trustworthy AI, providing the transparent audit trail—from source to consumption—needed for governance and compliance.

However, traditional lineage tools and even first-generation AI-SPM solutions fall short. They can discover AI models but fail to answer the most critical question: What specific data was this model trained on? For security teams governing thousands of models, this creates a massive security and compliance gap.

An advanced AI security platform bridges this gap. By correlating signals from data sources, code repositories, and the models themselves, it automatically reconstructs the data-to-model relationship. This creates a definitive, auditable trail from data origin to the final model version, providing the foundation for responsible and secure AI.

Accelerate Your AI Initiatives with Zero Trust

The era of AI demands an evolution in our security mindset. Simply knowing you have AI is not enough. A truly advanced AI-SPM framework must provide comprehensive visibility into the entire supply chain, proactively identify and manage model vulnerabilities, reconstruct data lineage for compliance, and enforce zero trust controls at the point of inference. As AI becomes more integrated into the fabric of your business, investing in an advanced AI-SPM strategy is not just a security measure—it's a critical enabler of innovation and trust.

Organizations can plan for advanced AI-SPM for complete visibility and protection across the entire AI ecosystem, ensuring security teams can:

• Discover and inventory AI models deployed across your cloud environments.
• Assess AI-specific risks, including data exposure, insecure model configurations and vulnerable dependencies.
• Monitor the AI supply chain to identify poisoned datasets or unauthorized models.
• Enforce governance policies for responsible AI use and regulatory compliance.
• Detect misconfigurations in AI workflows that could lead to sensitive data exposure.

These steps help apply the principles of a zero trust architecture to your use of AI applications, enabling security teams to:

• Deploy AI applications confidently: Implement AI with confidence and security assurance.
• Protect sensitive data: Prevent unauthorized access to data and context information.
• Enable secure innovations: Adopt new AI capabilities without compromising security.

Learn more at zscaler.com/security.