MCP Security

A protective layer that governs the context and tool interactions your AI systems rely on at runtime.

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# MCP Security

# A protective layer for context interactions

from sonnylabs import SonnyLabsClient

client = SonnyLabsClient(

api_token="your_token",

analysis_id="your_id"

)

# Inspect context at runtime

result = client.analyze_text(

user_input,

scan_type="input"

)

Runtime Analysis:

Context interaction verified

Session Protected

Runtime Protection For Model Context

SonnyLabs MCP Security provides a protective layer that governs the context and tool interactions your AI systems rely on at runtime. It monitors requests and responses across the model context layer, detecting manipulation, context interference and sensitive access attempts as they happen.

Audit Mode

Detect & log

Block Mode

Detect & prevent

🧠

Context Integrity

Monitors the context layer your AI systems operate within, identifying interference and manipulation as it occurs at runtime.

🔧

Tool Call Governance

Governs the tool interactions your systems rely on, providing visibility and control over what is being accessed and why.

📊

Runtime Monitoring

Live dashboard with logs and analytics for all activity across the context and tool-call layer.

🛡️

Manipulation Detection

Identifies attempts to manipulate the context and tool interactions your AI depends on.

🔒

Sensitive Access Control

Controls access to sensitive resources through the context and tool-call layer at runtime.

🌐

External Content Scanning

Scans external content that enters the context layer for threats before it reaches your systems.

How MCP Security Works

Simple Integration

# Install

pip install sonnylabs

# Protect in 3 lines

from sonnylabs import SonnyLabsClient

client = SonnyLabsClient(

api_token="your_token",

analysis_id="your_id"

)

# That's it - you're protected on the input

result = client.analyze_text(

user_input,

scan_type="input"

)

Real-time Analysis
MCP Security analyses every interaction across the context layer in real-time, identifying potential threats before they reach your systems.
Threat Detection Models
Our security models are specifically trained to detect context-layer and tool-call attacks and vulnerabilities.
Detailed Threat Reports
Get comprehensive information about detected threats, including type, severity, and mitigation recommendations.
Seamless Integration
Integrate with your existing runtime with just a few lines of code, with minimal latency impact.

Business Outcomes

Full visibility into context and tool access

See exactly what your AI systems are accessing through the context layer in real-time

Prevent unauthorized data access

Block attempts to access sensitive data through the context and tool-call layer

Demonstrate governance and control

Show auditors and customers you have control over your AI infrastructure

Meet enterprise security requirements

Satisfy vendor security assessments and compliance needs

Protect Your AI Infrastructure Today

Contact us to learn how SonnyLabs MCP Security can safeguard the context and tool interactions your AI systems depend on.

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Ready to Secure Your AI Applications?

Get in touch with our team to learn how SonnyLabs can help protect your AI systems