Backed by Cyber Security Agency of Singapore · CyberSG CRPO

The Firewall
for Generative AI

AI Guardian provides a unified safeguarding framework for Large Language Models — empowering organizations to deploy AI responsibly with robust protections, continuous monitoring, and actionable insights.

Explore Platforms Talk to Us
$151.9B GenAI market by 2032
31.4% CAGR growth rate
20+ AI safety publications
A* Top conference venues
The Problem

Gen AI is powerful — but vulnerable

Jailbreak attacks manipulate LLM inputs to bypass safety filters. Without a dedicated firewall, your AI system is exposed to misuse at scale.

Hate Speech & Harassment Violence & Self-Harm Impersonation & Deception Illegal Activities Misinformation Sexually Explicit Content Malicious Code Religious/Political Extremism

🔥 We need a new FIREWALL for Gen AI

Traditional cybersecurity was never built for language models. Attackers craft adversarial prompts that slip past model guardrails — putting users, brands, and regulators at risk.

Stakeholder Consequences
UsersPsychological harm, manipulation
DevelopersLegal risk, reputational loss
IndustryData leaks, non-compliance
GovernmentsElection threats, instability
Our Platforms

Two layers of AI protection

Probe finds vulnerabilities before they reach production. Shield stops threats in real time as your AI operates.

🔴
Probe

AI Red Teaming & Risk Assessment

Automated red teaming that exposes vulnerabilities of large language models across diverse attack scenarios — before they reach production. Generates a comprehensive risk report with targeted mitigation strategies.

  • API Integration — test commercial (GPT-4o, O3, Claude) and open-source models (DeepSeek, Llama, Qwen) via API
  • Multi-Category Safety Assessment — hate speech, privacy, child exploitation, illegal activities and more
  • Detailed Reporting — performance insights, vulnerabilities, and safety compliance metrics
  • Historical Tracking — monitor safety improvements or degradation over time
  • Cross-Model Benchmarking — compare safety performance across LLMs to identify the most secure option
🛡️
Shield

Real-Time AI Guardrails

Non-tuning-based prompt-level defences that integrate with any off-the-shelf LLM without modifying internal parameters. Dual input/output filter stops threats both entering and leaving your AI.

  • Input filter: detects and blocks malicious queries
  • Output filter: catches restricted or harmful responses
  • No model retraining required
  • High-accuracy, low-latency threat blocking
  • SaaS or on-premise subscription model
Key Advantages

Security designed for LLMs

Each platform is purpose-built for the unique threat landscape of generative AI.

⬛

Black-Box Testing

No access to internal model states required. Test any LLM through standard API interactions — works with proprietary and open-source models alike.

🎯

Wide Attack Coverage

Comprehensive testing across diverse attack scenarios — hate speech, illegal activities, malicious code, self-harm, and more. Delivered with a quantitative risk assessment report.

☁️

Flexible Deployment

Cloud-based, on-premise, or local deployment options. Making safety accessible at every stage of development.

🔒

Non-Finetuning Defence

Prompt-level protection that works with any off-the-shelf LLM without requiring model retraining or access to internal parameters.

⚡

Real-Time Protection

Speedy threat blocking during live operations with high accuracy. Both input and output filters operate concurrently to maximize coverage.

🏗️

Flexible Deployment

Cloud-based, on-premise, or your own cloud — with subscription and maintenance/integration support from the AI Guardian team.

How It Works

End-to-end AI safeguarding

From pre-deployment red teaming to live runtime protection — AI Guardian covers the full LLM security lifecycle.

Probe — Red Teaming Pipeline
💀
Probe
Generate Adversarial Inputs

Creates a diverse set of malicious prompts targeting potential vulnerabilities

→
🤖
Tested AI Agent
Query & Respond

LLM receives adversarial queries and generates responses via API

→
🔍
Compliance Check
Evaluate Responses

Analyses outputs for harmful content, policy violations, and risk signals

→
📊
Risk Report
Quantified Results

Comprehensive report with vulnerability scores and mitigation recommendations

Shield — Runtime Protection
👤
Agent Users

User sends a prompt to the AI system

→
🔽
① Input Filter

Screens for malicious queries before reaching the LLM

→
🤖
AI Agent

Benign query processed; LLM generates response

→
🔼
② Output Filter

Intercepts restricted or harmful LLM responses

→
✅
Safe Response

Verified, clean response returned to the user

🚫
When a threat is detected at ① or ②
The query or response is blocked immediately and the user receives: "Sorry, I cannot assist with this request."
8+ Threat categories
covered
10+ LLMs tested
(GPT, Mistral, LLaMA…)
20+ AI safety research
publications
A* Publications in CVPR,
NeurIPS, ICML, ICCV
Research

Theory-grounded AI safety

Our platforms are built on peer-reviewed research across adversarial robustness, natural robustness, and uncertainty quantification.

⚔️

Adversarial Robustness

Research on feature-space attacks, gradient inversion, adversarial camouflage, and federated learning vulnerabilities published in top IEEE journals.

🌍

Natural Robustness

Domain generalization, sharpness-aware minimization, trustworthy multi-view classification, and long-tailed classification.

📊

Uncertainty Quantification

Out-of-distribution detection, calibrated multimodal learning, and confidence-aware screening for reliable AI decision-making.

IEEE TCyb A Feature Space-Restricted Attention Attack on Medical Deep Learning System — CiteScore top 1%, 2022
CVPR A* Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection — 2022
ICCV A* Generative Gradient Inversion via Over-Parameterized Convolutional Networks in Federated Learning — 2023
NeurIPS A* Out-Of-Distribution Detection with Diversification (Provably) — 2023
Nature Comms Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification — 2023
ICML A* Calibrating Multimodal Learning — 2023
IEEE TNNLS Generative Image Reconstruction from Gradients — CiteScore top 1%, 2025
About

Mission & Vision

Mission

Enable safe and trustworthy AI adoption by delivering an integrated safeguarding framework that protects LLMs from misuse, enhances resilience, and ensures compliance with ethical and regulatory standards.

Vision

To be the global standard for LLM safeguarding, where every AI system can operate securely, transparently, and responsibly — fostering innovation without compromising safety.

.

Core Values

🤝

Trustworthiness

Building confidence through transparency and accountability in every deployment.

💡

Innovation with Responsibility

Advancing AI capabilities while embedding safety at every layer of the stack.

🌐

Collaboration

Partnering with researchers, enterprises, and regulators to set global safeguards.

🛡️

Resilience

Ensuring systems adapt and withstand the evolving landscape of AI threats.

Recognition

Backed by leading institutions

🏛️ CyberSG CRPO Grand Challenge Award

Powered by technology developed through the CyberSG R&D Programme Office (CRPO) Grand Challenge Award Project (Award No.: CRPO-GC1-NTU-002). Supported by the Cyber Security Agency of Singapore.

🎓 NTU & A*STAR IHPC Research Pedigree

Built on research from Nanyang Technological University and the A*STAR Institute of High Performance Computing (IHPC) — led by Dr. Liangli Zhen, PhD, Group Manager & Senior Scientist at IHPC. Singapore's premier computational AI research institution, producing publications in CVPR, NeurIPS, ICML, ICCV, and top-1% IEEE journals.

📚 Top-1% Publication Record

Research published in IEEE TPAMI, IEEE TNNLS, IEEE TCyb, and Medical Image Analysis — all CiteScore top 1% journals — alongside A* conferences including CVPR, NeurIPS, ICML, and ICCV.

🌏 Singapore AI Safety Leadership

Positioned at the intersection of Southeast Asia's growing AI market and Singapore's world-class cybersecurity ecosystem — building the safeguarding infrastructure the region needs.

🏆 AISG Global Challenge Awardee

2nd Prize (Attack Track, 2024) and 3rd Prize (Defence Track, 2025) at the AISG Global Challenge for Safe and Secure LLMs — competing against 70–100+ international teams. Directly validates the technology behind Probe and Shield.

Market Opportunity

Autonomous AI Adoption Accelerating,
Trust Determines Success

The window for establishing AI governance standards is now. Enterprises are deploying agents faster than security frameworks can keep up.

40%
by 2026

of Enterprise Applications will feature Agentic AI Capabilities

Source: Gartner 2025
40%
by 2027

of Enterprise Applications will Experience Issues to Scale due to trust failures

Source: Gartner 2026
$492M→>$1B
2026 → 2030

Global spending on AI Governance (USD) growing rapidly

Source: McKinsey 2026
AI Guardian Key Industries
🏦 Financial Services & Insurance
🏥 Healthcare
💬 Customer Service
📡 Telecommunications
🏛️ Public Sector
Our Team

The people behind AI Guardian

A unique combination of entrepreneurial leadership and world-class AI safety research.

Co-Founder · Chief Executive Officer

Jovian Wee

A serial entrepreneur with a proven track record of launching and scaling multiple businesses in licensing, specialized product distribution and MedTech commercialization. His expertise in navigating complex market dynamics and forging strategic partnerships has positioned AI Guardian for rapid growth and success in the AI-driven safety sector.

Co-Founder · Chief Operating Officer

Suveer Sachdev

B.S. Biomedical Engineering · Georgia Institute of Technology

A Georgia Tech–trained Biomedical Engineer who graduated with Faculty Honors (4.0 GPA, Fall 2019 & Spring 2020). Brings a rigorous analytical foundation to AI Guardian's operations — translating cutting-edge AI safety research into scalable, enterprise-ready processes and delivery frameworks.

Faculty Honors · Spring 2020 Georgia Tech
Co-Founding Scientific Advisor

Dr. Liangli Zhen, PhD

Group Manager & Senior Scientist · A*STAR IHPC

Group Leader of the AI Safety Research Team at IHPC, A*STAR — devoted to improving the safety and robustness of AI for real-world systems. Leading researcher in adversarial robustness and multimodal learning, with publications in CVPR, NeurIPS, ICML, ICCV, and top-1% IEEE journals.

Co-Founding Scientific Advisor

Prof. Rick Goh, PhD

Duke-NUS Medical School · A*STAR IHPC Director

Driving AI innovations in the healthcare sector. Brings deep expertise in artificial intelligence, medical imaging, computational modeling, and translational healthcare innovation. Recognized for pioneering AI-driven diagnostics, advancing clinical validation frameworks, and shaping cross-disciplinary collaborations between academia, industry, and government.

Co-Founding Scientific Advisor

Prof. Liu Yong, PhD

Duke-NUS Medical School · A*STAR IHPC Deputy Director

Brings extensive expertise in artificial intelligence, computational modelling, and large-scale algorithm design for healthcare applications. Recognized for advancing AI-driven medical imaging, predictive analytics, and clinical decision-support systems. Plays a pivotal role in bridging research excellence with translational healthcare innovation, regulatory alignment, and enterprise adoption.

Recognition

Award-winning AI safety research

Validated by the world's toughest AI safety challenges — competing against teams from across the globe.

🏆 AISG Global Challenge

Award in AISG Global Challenge
for Safe and Secure LLMs

This challenge focuses on addressing the vulnerabilities of large language models that can be exploited through jailbreak attacks — competing against over 100 teams from around the globe.

Track 1 — Attack · Oct 2024
🥈 2nd Prize
Design automated approaches to craft test cases that trigger undesirable responses from LLMs
70+ competing teams globally
Track 2 — Defence · May 2025
🥉 3rd Prize
Develop robust guardrails to prevent LLMs from advanced jailbreaking attacks
100+ competing teams globally
Live Demo

Try Probe — right now

Enter any prompt below and Probe will instantly assess whether it contains malicious intent, jailbreak attempts, or policy violations.

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