Introducing

Beyond Guard

Red Teaming

Proactive security testing for enterprise GenAI, RAG, and agentic systems - from scenarios to executive-ready reports.

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Release: Q4 2025 | Module: Governance > Red Teaming

Why Red Teaming for AI

As GenAI moves into production - powering copilots, RAG-based knowledge assistants, and autonomous agents - the attack surface extends beyond traditional application security.

Prompts, retrieved context, tool calls, and model outputs can be manipulated in ways that lead to unsafe behavior, policy bypass, or unintended exposure of sensitive data.

AI red teaming is a proactive approach that tests AI systems against realistic adversarial and misuse scenarios, helping teams identify weaknesses and control gaps before they reach end users.

What Beyond Guard Red Teaming Delivers

Beyond Guard Red Teaming is designed for security and governance teams that need repeatable, auditable testing across multiple LLM providers (cloud or on-prem) and multiple AI application patterns.

Scenario library and custom scenarios to reflect real-world threats and your internal policies.

❖ Test case management with single entry or bulk import via CSV (content, expected_result, severity_level).

❖ Test creation and execution with endpoint and authorization parameters.

❖ Execution visibility with step-level status and progress tracking.

❖ Report preview and one-click PDF export for stakeholders and audit readiness.

How it Works

Beyond Guard Red Teaming follows a two-stage evaluation workflow:

❖ Stage 1 - Target model run: scenario prompts are sent to the selected LLM, and responses are captured end-to-end.

❖ Stage 2 - Judge evaluation: captured responses are assessed by the Beyond Guard Judge Model from a red team perspective, including policy alignment, potential risks, violation paths, and attack surface analysis.

This approach evaluates not only what the model answers, but also what the answer can enable in terms of security, governance, and resilience. The results feed directly into professional red team reports.

Turn Testing Into Control

Many organizations have begun testing their GenAI systems. Fewer have established a repeatable process that supports governance, accountability, and long-term resilience.

Red teaming delivers value when findings inform decisions: risk prioritization, policy development, investment planning, and product direction.

The progression is clear — from individual test runs to a sustainable operational practice that aligns with enterprise standards.

Download our complete Red Team Guide to start building disciplined, audit-ready testing practices across your GenAI applications.

Let's talk

Don’t let a prompt become a breach

Book a demo

Let's talk

Don’t let a prompt become a breach

Book a demo

Let's talk

Don’t let a prompt become a breach

Book a demo

Let's talk

Don’t let a prompt become a breach

Book a demo

Introducing

Beyond Guard

Red Teaming

Why Red Teaming for AI

As GenAI moves into production - powering copilots, RAG-based knowledge assistants, and autonomous agents - the attack surface extends beyond traditional application security.

Prompts, retrieved context, tool calls, and model outputs can be manipulated in ways that lead to unsafe behavior, policy bypass, or unintended exposure of sensitive data.

AI red teaming is a proactive approach that tests AI systems against realistic adversarial and misuse scenarios, helping teams identify weaknesses and control gaps before they reach end users.

What Beyond Guard Red Teaming Delivers

Beyond Guard Red Teaming is designed for security and governance teams that need repeatable, auditable testing across multiple LLM providers (cloud or on-prem) and multiple AI application patterns.

Scenario library and custom scenarios to reflect real-world threats and your internal policies.❖ Test case management with single entry or bulk import via CSV (content, expected_result, severity_level).

❖ Test creation and execution with endpoint and authorization parameters.

❖ Execution visibility with step-level status and progress tracking.

❖ Report preview and one-click PDF export for stakeholders and audit readiness.

How it Works

Beyond Guard Red Teaming follows a two-stage evaluation workflow:

❖ Stage 1 - Target model run: scenario prompts are sent to the selected LLM, and responses are captured end-to-end.

❖ Stage 2 - Judge evaluation: captured responses are assessed by the Beyond Guard Judge Model from a red team perspective, including policy alignment, potential risks, violation paths, and attack surface analysis.

This approach evaluates not only what the model answers, but also what the answer can enable in terms of security, governance, and resilience. The results feed directly into professional red team reports.

Turn Testing Into Control

Many organizations have begun testing their GenAI systems. Fewer have established a repeatable process that supports governance, accountability, and long-term resilience.

Don’t let a prompt become a breach

Book a demo

Don’t let a prompt become a breach

Book a demo

Don’t let a prompt become a breach

Book a demo

Don’t let a prompt become a breach

Book a demo

Product

Company

Legal & security

Product

Company

Legal & security

Product

Company

Legal & security

Product

Company

Legal & security

Scenario library and custom scenarios to reflect real-world threats and your internal policies.

❖ Test case management with single entry or bulk import via CSV (content, expected_result, severity_level).