> ## Documentation Index
> Fetch the complete documentation index at: https://docs.enkryptai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Research

> Latest research findings, papers, and studies on AI safety and security

## Access Our Research

**[Explore Our Research Publications →](https://www.enkryptai.com/research)**

Stay informed about the latest breakthroughs in AI safety research and contribute to building a safer AI ecosystem.

## Advancing AI Safety Through Research

Our research team conducts cutting-edge studies on AI safety, security, and risk assessment methodologies. We publish our findings to advance the field and help the community build safer AI systems.

### Key Publications

#### **Guardrail Research**

**"No Free Lunch with Guardrail"**
Benchmarks show stronger guardrails improve safety but can reduce usability. Our paper proposes a framework to balance the trade-offs — ensuring practical, secure LLM deployment.

**"Fine-Tuning, Quantization & Safety"**
Fine-tuning increases jailbreak vulnerability, while quantization has varied effects. Our analysis emphasizes the role of strong guardrails in deployment.

#### **Bias and Fairness Research**

**"Investigating Implicit Bias in LLMs"**
A study of 50+ models reveals that bias persists — and sometimes worsens — in newer models. The work calls for standardized benchmarks to prevent discrimination in real-world AI use.

#### **RAG and Generation Research**

**"VERA: Validation & Enhancement for RAG"**
VERA improves Retrieval-Augmented Generation by refining retrieved context and output, reducing hallucinations and enhancing response quality across open-source and commercial models.

#### **Red Teaming Research**

**"SAGE-RT Synthetic Red Teaming"**
SAGE enables scalable, synthetic red-teaming across 1,500+ harmfulness categories — achieving 100% jailbreak success on GPT-4o and GPT-3.5 in key scenarios.

### AI Guardrail Benchmark Studies

Our research goes beyond just publications – it's been applied to real-world benchmark studies to evaluate the security and performance of leading AI guardrails. These comparative tests provide practical insights into how guardrails perform under real attack scenarios.

#### **Comparative Guardrail Evaluations**

* **Enkrypt AI vs Guardrails AI vs Protect AI LLM Guard** - Comprehensive comparison of leading guardrail solutions
* **Enkrypt AI vs Azure Content Safety vs Amazon Bedrock Guardrails** - Cloud provider guardrail analysis
* **Enkrypt AI vs IBM Granite vs Azure AI vs Prompt Shield vs Amazon Bedrock Guardrails** - Multi-vendor security assessment

### Building Safer AI from the Ground Up

#### **Securing LLM Providers**

Enkrypt AI tests over 100 leading foundation models - including from AI21, DeepSeek, Databricks, and Mistral - to strengthen the safety of their LLMs without compromising performance.

#### **Comprehensive Testing Framework**

We conduct more than 50,000 dynamic red-teaming evaluations per model, spanning critical risk categories:

* **Bias Detection** - Gender, race, religion, and other discriminatory biases
* **Insecure Code** - Vulnerable code generation and security flaws
* **CBRN Threats** - Chemical, biological, radiological, and nuclear risks
* **Harmful Content** - Toxic, violent, and inappropriate content
* **Toxicity Assessment** - Offensive language and harmful speech
* **Regulated Substances** - Controlled and illegal substance content
* **Guns & Illegal Weapons** - Weapon-related content detection
* **Criminal Planning** - Illegal activities and malicious intent
* **Suicide & Self-harm** - Self-destructive content identification
* **Sexual Content** - Inappropriate sexual material detection

### Research Impact

Our research advancements fuel our security platform and power the **LLM Safety & Security Leaderboard** - the most comprehensive benchmark for model safety in the industry.

***

*Our research is conducted in collaboration with leading universities, research institutions, and industry partners worldwide.*