AI Security

Protecting Intelligent Systems

AI systems face unique security challenges—from prompt injection and model poisoning to adversarial attacks and autonomous agent hijacking. We build comprehensive defenses for every attack vector.

99.7%

Attack Detection Rate

<50ms

Response Latency

Zero

False Positives (Tuned)

The AI Threat Landscape

Understanding AI-Specific Attacks

Traditional security tools weren't designed for AI systems. New attack vectors require new defenses built specifically for machine learning and autonomous agents.

Critical

Prompt Injection

Malicious instructions embedded in inputs that hijack AI behavior, bypass safety measures, or extract sensitive information.

Direct injection via user input

Indirect injection via external data

Jailbreaking and safety bypass

Our Protection:

Multi-layer input validation and semantic analysis

High

Model Poisoning

Corrupting training data or fine-tuning processes to introduce backdoors, biases, or malicious behaviors into AI models.

Training data poisoning

Backdoor insertion

Gradient manipulation

Our Protection:

Data provenance tracking and model integrity verification

High

Adversarial Attacks

Carefully crafted inputs designed to fool AI models into making incorrect predictions or classifications.

Evasion attacks

Perturbation attacks

Transferability attacks

Our Protection:

Adversarial training and robust model architectures

Critical

Agent Hijacking

Compromising autonomous agents to perform unauthorized actions, exfiltrate data, or propagate attacks across systems.

Identity spoofing

Capability escalation

Cross-agent propagation

Our Protection:

Cryptographic identity and behavioral anomaly detection

High

Data Exfiltration

Extracting sensitive information from AI systems including training data, model parameters, or processed information.

Membership inference

Model inversion

Training data extraction

Our Protection:

Differential privacy and output monitoring

Medium

Supply Chain Attacks

Compromising AI components, libraries, or pre-trained models before they reach production environments.

Malicious model repositories

Compromised dependencies

Trojan models

Our Protection:

Secure model provenance and dependency scanning

Defense in Depth

Our AI Security Stack

Multiple layers of protection working together to secure every aspect of your AI systems.

Layer 1

Input Validation & Sanitization

First line of defense analyzing all inputs before they reach AI models

Semantic analysisPattern detectionInjection filteringRate limiting

Layer 2

Model Protection

Runtime protection for AI models and their execution environment

Integrity verificationExecution sandboxingOutput validationAnomaly detection

Layer 3

Agent Security

Identity, authentication, and behavioral monitoring for autonomous agents

Cryptographic identityCapability enforcementBehavior analysisTrust scoring

Layer 4

Data Protection

Protecting sensitive data throughout the AI lifecycle

Differential privacyEncryption at restAccess controlAudit logging

Capabilities

Comprehensive AI Protection

Prompt Injection Firewall

Real-time detection and blocking of prompt injection attacks using semantic analysis and pattern matching.

Model Integrity Monitoring

Continuous verification that AI models haven't been tampered with, poisoned, or compromised.

Real-Time Threat Response

Automated response to detected threats including isolation, alerting, and remediation.

Behavioral Anomaly Detection

ML-based detection of unusual agent behaviors that may indicate compromise or manipulation.

Secure Agent Communication

End-to-end encrypted communication between agents with mutual authentication.

Execution Sandboxing

Isolated execution environments that contain potential damage from compromised AI components.

Easy Integration

Deploy AI Security in Minutes

Our security infrastructure integrates seamlessly with your existing AI stack—whether you're using OpenAI, Anthropic, open-source models, or custom deployments.

Drop-in SDKs for Python, TypeScript, Go, and Rust

Native integration with popular AI frameworks

REST and GraphQL APIs for custom integrations

Kubernetes operators for cloud-native deployments

View Documentation

ai_security.py

from gqt_security import AISecurityClient

# Initialize security client
security = AISecurityClient(api_key="your-key")

# Wrap your AI calls with protection
@security.protect(
    prompt_injection=True,
    output_validation=True,
    anomaly_detection=True
)
async def secure_ai_call(prompt: str):
    response = await openai.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    return response

# All attacks are automatically blocked
result = await secure_ai_call(user_input)

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