Enterprise AI Red Teaming Guide
AI systems now sit inside core business functions and influence decisions, automate workflows, summarize sensitive information, and take action through tools. As enterprises integrate Large Language Models (LLMs) into core business logic, they introduce non-deterministic risks that traditional software testing cannot detect. AI Red Teaming addresses these risks through structured adversarial evaluation, simulating attacks on […]
Case Study: Implementing Automated Red Teaming with Advanced Metaprompting
Let’s walk through how TestSavant’s red-teaming service implements meta prompting into the RedSavant automated red teaming product.. We built our entire system using a sophisticated, multi-layered meta-prompting approach. This allows our process to be highly adaptable and context-aware, moving far beyond generic tests. The following is a simplified version of our system. Although there are […]
Metaprompting: The Architecture of Automated AI Red Teaming
Architect automated AI Red Teaming. This posts walks through each stage of the pipeline, from context ingestion to probe generation, scoring, and mitigation planning, showing how enterprises can scale high-quality AI safety testing.
AI Red Teaming Fundamentals
AI systems getting deployed in the enterprise today support core business operations across customer service, legal review, software development, financial analysis, internal knowledge search, and operational workflows. As these systems handle increasingly sensitive data, take action through tools, and influence decisions, you need to understand how they behave when exposed to challenging input or adversarial […]
AI Red Teaming 101: What is Red Teaming?
For decades, red teaming meant simulating real-world attackers to test how strong an organization’s defenses really were. The practice started in military planning, then took root in cybersecurity as a way to “think like the enemy” and reveal weaknesses that compliance checks or penetration tests might miss. Where penetration testing tends to look for known […]
Computer Says “No” Isn’t an Explanation: Turning Legal Duties into Runtime Evidence for AI and Agents
If your AI system denies a loan, flags an intake, or blocks an agentic action, could you produce a clear, human-readable explanation that stands up to a regulator, a judge, and the person impacted—without revealing trade secrets—today?
How to Red Team Prompt Injection Attacks on Multi-Modal LLM Agents
Fundamentals of Prompt Injections Definition and Historical Evolution Prompt injection is a security vulnerability where malicious input is injected into an AI’s prompt, causing the model to follow the attacker’s instructions instead of the original intent. The term prompt injection was coined in September 2022 by Simon Willison, drawing analogy to SQL injection attacks in […]
TestSavant.AI’s Unified Guardrail Model: A Lightpaper
TestSavant.AI’s Unified Guardrail Model represents a comprehensive, consolidated security solution. By unifying multiple defense layers into a single model
Region-by-Region Playbook for Generative AI Risk Compliance in 2025
Generative AI no longer sits on the fringes of experimentation. It’s deeply woven into underwriting processes, contract reviews, advanced research, and more. Meanwhile
Securing Your AI: Introducing Our Guardrail Models on HuggingFace
Enterprise AI teams are moving fast, often under intense pressure to deliver transformative solutions on tight deadlines. With that pace comes a serious security challenge: prompt injection and jailbreak attacks that can cause large language models (LLMs) to leak sensitive data or produce disallowed content. Senior leaders and CISOs don’t have the luxury of ignoring these threats.