PRISM AI Discovery: Complete Guide to AI Attack Surface Visibility

 

PRISM AI Discovery

Introduction

Artificial intelligence is rapidly becoming part of everyday business operations. Organizations are deploying large language models (LLMs), AI agents, machine learning systems, APIs, model endpoints, and connected tools across cloud, on-premises, and hybrid environments.

But as AI adoption grows, a new cybersecurity problem is emerging: organizations often do not know exactly how many AI systems are operating inside their environment.

An approved AI application may be documented by the security team, while an employee uses an external AI service, a development team deploys a model, or an autonomous agent connects to an MCP server without appearing in traditional IT asset inventories.

This creates AI attack surface visibility gaps.

SISA PRISM addresses this challenge through its PRISM AI Attack Surface Visibility, also referred to as PrismDiscover. The solution is designed to discover and catalogue AI models, autonomous agents, MCP servers, API endpoints, and traditional ML systems across cloud, on-premises, and hybrid environments.

What Is PRISM AI Discovery?

PRISM AI Discovery is an AI-native discovery capability designed to help organizations identify and map the AI assets operating across their enterprise.

Traditional IT asset management and configuration management databases may provide visibility into servers, applications, and conventional infrastructure. However, they may not provide sufficient visibility into newer AI-specific assets such as autonomous agents, model lineage, MCP servers, agent permissions, and inter-agent communication.

Prism Discover addresses this gap by creating an AI-focused inventory across five discovery domains:

• AI and LLM models
• Autonomous AI agents
• MCP servers
• API endpoints
• Traditional machine learning systems

The platform can discover these assets across cloud, on-premises, and hybrid environments, creating a centralized AI asset register.

Why AI Discovery Matters

You cannot secure an AI system that you do not know exists.

As businesses experiment with different AI platforms and models, AI deployments can quickly become decentralized. Development teams may introduce models into applications, employees may use external AI services, and autonomous agents may gain access to business tools.

This can create Shadow AI — AI technology operating without appropriate security or governance oversight.

SISA identifies several challenges associated with this environment, including incomplete inventories, invisible agent and MCP trust relationships, and governance outputs that are not immediately aligned with AI regulations and frameworks.

AI discovery therefore becomes the starting point for AI security.

Before organizations can test, monitor, govern, or protect their AI systems, they need to establish an accurate inventory.

The Five Domains of PRISM AI Discovery

1. AI and LLM Models

The first area focuses on identifying AI and large language models operating throughout the enterprise.

Discovery goes beyond simply identifying a model. PrismDiscover is designed to surface fine-tuning lineage, providing greater visibility into how models may have been customized or adapted.

This can help security and AI teams understand:

• Which models are being used
• Where models are deployed
• How models have been customized
• Which systems depend on them
• Whether they are operating in cloud, on-premises, or hybrid environments

This information provides an important foundation for model-level security assessment.

2. Autonomous AI Agents

AI agents are becoming increasingly capable of performing tasks rather than simply generating responses.

An agent may be able to interact with applications, access information, call APIs, or use external tools.

That makes understanding agent permissions particularly important.

PRISM AI Discovery identifies autonomous agents and maps their:

• Skills
• Permissions
• Agent frameworks
• Inter-agent communication
• Connected systems

This provides security teams with visibility into how agents operate and what they are authorized to access.

3. MCP Servers

Model Context Protocol (MCP) servers are becoming an important part of modern AI ecosystems because they can provide AI systems with access to external tools and data.

However, these connections can also create new trust relationships.

An overly privileged or poorly governed MCP connection could potentially expand the attack surface of an AI application.

PRISM AI Discovery is designed to identify MCP servers connected to AI agents and map their trust chains, external integrations, and data access relationships.

This helps organizations understand not only what AI assets exist, but also how those assets are connected.

4. API Endpoints

AI applications frequently communicate through APIs.

These APIs can connect models and agents with:

• Business applications
• Databases
• Cloud services
• Internal platforms
• External applications
• Enterprise workflows

PRISM AI Discovery includes API endpoints as part of its AI attack surface inventory.

Knowing which endpoints are associated with AI systems can help security teams identify systems that require additional security testing, authentication controls, monitoring, or governance.

5. Traditional Machine Learning

AI security is not limited to generative AI.

Organizations continue to operate traditional machine learning systems for areas such as analytics, fraud detection, recommendation systems, forecasting, and risk assessment.

PRISM AI Discovery includes traditional ML within its five-domain inventory, helping organizations maintain visibility across both modern and conventional AI environments.

How PRISM AI Discovery Works

PRISM describes its discovery process through four main stages:

Catalogue → Map → Verify → Govern

Each stage builds on the previous one.

Step 1: Catalogue

The first step is to identify AI assets throughout the enterprise.

PRISM AI Discovery can catalogue models, agents, MCP servers, API endpoints, and traditional ML across cloud, on-premises, and hybrid environments.

The result is a centralized AI inventory rather than a collection of disconnected spreadsheets.

Step 2: Map

Finding AI assets is only the beginning.

Organizations also need to understand relationships between those assets.

PRISM maps areas such as:

• Fine-tuning lineage
• Agent skills
• Agent permissions
• Inter-agent communication
• MCP server trust chains

This helps security teams understand the relationships and dependencies within their AI environment.

Step 3: Verify

AI environments are constantly changing.

New models are deployed, agents are created, APIs are connected, and applications evolve.

For this reason, a point-in-time inventory can quickly become outdated.

PRISM’s discovery approach emphasizes continuous discovery rather than a one-time export, allowing organizations to maintain more current visibility into their AI attack surface.

SISA also identifies supply-chain integrity capabilities such as model provenance, signed weights, checksums, and AI library dependency scanning as roadmap capabilities for the discovery solution.

Step 4: Govern

Once AI assets have been identified and mapped, organizations need to manage them from a governance perspective.

PRISM AI Discovery produces a governance-ready AI register aligned with frameworks and regulatory requirements including:

• ISO 42001
• EU AI Act
• NIST AI RMF
• CBUAE
• SAMA

The inventory can then feed other PRISM modules for testing, scanning, runtime monitoring, and governance activities.

Eliminating Shadow AI Blind Spots

Shadow AI is one of the biggest challenges associated with rapid AI adoption.

Employees may use AI tools without formally notifying security teams. Developers may deploy models as part of applications. Teams may connect AI agents to external services without creating centralized documentation.

Traditional IT inventories can struggle to capture these assets.

PRISM AI Discovery is designed to identify AI assets beyond officially approved deployments, helping organizations create a more complete picture of their AI environment.

This visibility can help organizations answer questions such as:

• How many AI systems are operating?
• Which teams own them?
• Which models are being used?
• Which agents have elevated permissions?
• Which MCP servers are connected?
• Which APIs are exposed?
• What data can AI systems access?

Why Agent and MCP Visibility Is Important

AI agents introduce a new dimension to enterprise security.

A conventional application generally performs functions defined by developers. An AI agent can interpret instructions, select tools, and perform tasks based on its objectives and available permissions.

If multiple agents communicate with each other, the security picture becomes even more complicated.

MCP servers can further expand these relationships by providing agents with access to tools and information.

PRISM’s ability to map agent permissions, inter-agent communication, and MCP trust chains is designed to make these relationships visible to security architects and CISOs.

Benefits of PRISM AI Discovery

Complete AI Asset Visibility

Organizations can build a centralized inventory covering models, agents, MCP servers, APIs, and ML systems.

Reduced Shadow AI Risk

Discovery can help identify AI deployments that may otherwise remain outside conventional security inventories.

Better Understanding of AI Relationships

Mapping agent permissions, model lineage, and MCP trust relationships provides greater context around the attack surface.

Continuous Visibility

Continuous discovery helps reduce the risk of relying on outdated spreadsheets or point-in-time inventories.

Governance Readiness

AI asset information can be structured around major AI governance frameworks and regulations.

Foundation for AI Security

The discovered inventory can feed downstream PRISM capabilities for adversarial testing, ML artifact scanning, runtime monitoring, and AI governance.

Who Can Benefit From PRISM AI Discovery?

CISOs

Security leaders gain visibility into the organization’s overall AI attack surface and can establish a more complete AI asset inventory.

Security Architects

Security teams can analyze agent permissions, communication flows, and MCP trust relationships that may not appear in conventional IT management tools.

Compliance and Risk Teams

Compliance teams can use structured AI inventory information aligned with major AI governance frameworks.

AI and ML Teams

AI teams can maintain visibility into models, agents, APIs, and other components while feeding discovered assets into downstream security processes.

PRISM AI Discovery and the Broader AI Security Lifecycle

Discovery is only the first step in securing AI.

SISA PRISM describes a broader AI security platform that connects discovery with additional capabilities, including adversarial testing, ML artifact and supply-chain integrity, in-model LLM hardening, runtime security and observability, and AI GRC automation.

This creates a lifecycle approach:

Discover → Test → Scan → Harden → Monitor → Govern

The benefit of connecting these stages is that security teams do not have to treat AI inventory, testing, monitoring, and compliance as completely separate processes.

Conclusion

As AI becomes embedded in enterprise applications and workflows, organizations need more than traditional IT asset management to understand their technology environment.

PRISM AI Discovery provides an AI-native approach to discovering and mapping the modern AI attack surface. By cataloguing models, autonomous agents, MCP servers, API endpoints, and traditional ML across cloud, on-premises, and hybrid environments, organizations can establish a more complete picture of where AI is operating.

The most important principle is simple:

You cannot secure what you cannot see.

By creating continuous AI asset visibility, mapping agent and MCP relationships, and connecting discovery with security testing and governance, organizations can move from an incomplete AI inventory toward a more proactive and measurable AI security strategy.