GenAI Security Framework Assessment

Protect your LLM deployments from emerging threats

🛡️ OWASP Top 10 for LLMs 🔒 Prompt Injection Defense 📊 Data Privacy Controls

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Critical GenAI Security Threats

💉

Prompt Injection

Malicious inputs that manipulate LLM behavior to bypass controls or extract sensitive data

📊

Data Leakage

Unintended exposure of training data or confidential information through model outputs

🎭

Model Inversion

Attacks that reconstruct training data or extract proprietary information from the model

🔄

Supply Chain Attacks

Compromised models, datasets, or dependencies introducing vulnerabilities

💰

Resource Abuse

Denial of service or excessive API consumption leading to cost overruns

⚖️

Compliance Violations

Generating content that violates regulations, copyrights, or ethical guidelines

GenAI Security Framework Pillars

🔐

Input Security

Prompt validation and sanitization
Input length and complexity limits
Content filtering and moderation
Injection attack detection
User authentication and authorization

🛡️

Model Security

Model access controls and encryption
Version control and integrity checks
Adversarial robustness testing
Model poisoning prevention
Secure model deployment pipelines

📋

Output Controls

Response filtering and validation
PII and sensitive data detection
Hallucination detection mechanisms
Copyright and license compliance
Output quality assurance

📊

Data Governance

Training data classification
Data retention and deletion policies
Privacy-preserving techniques
Consent management
Cross-border data compliance

🔍

Monitoring & Audit

Real-time threat detection
Usage pattern analysis
Audit trail maintenance
Incident response procedures
Performance and drift monitoring

⚖️

Compliance & Ethics

Regulatory compliance mapping
Ethical AI guidelines
Bias detection and mitigation
Transparency and explainability
Human oversight mechanisms

Security Controls Assessment

🚪

Access Controls

API key management
Rate limiting and throttling
Role-based access control
Session management
Multi-factor authentication

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Encryption

Data encryption at rest
TLS for data in transit
Model encryption
Key management systems
Secure enclaves

📝

Logging & Monitoring

Comprehensive audit logs
Real-time alerting
Anomaly detection
Security dashboards
SIEM integration

GenAI Security Maturity Levels

Initial

Ad-hoc security measures

Developing

Basic controls in place

Defined

Documented policies

Managed

Proactive monitoring

Optimized

Continuous improvement

Real-World Impact

🏦

Global Financial Services

Banking & Insurance

"After implementing the GenAI Security Framework, we prevented 3 major prompt injection attempts in the first month alone. The assessment revealed critical gaps in our LLM deployment that we weren't aware of. The structured approach to security has given us confidence to expand our AI initiatives."

99.9%

Attack Prevention

85%

Risk Reduction

Faster Deployment

100%

Compliance

Frequently Asked Questions

What is GenAI security?

GenAI security encompasses the practices, controls, and technologies used to protect generative AI systems like LLMs from threats including prompt injection, data leakage, model manipulation, and compliance violations.

Why is prompt injection a critical threat?

Prompt injection can bypass security controls, extract sensitive information, manipulate model outputs, and potentially compromise entire systems. It's considered the #1 threat in the OWASP Top 10 for LLM applications.

How does this assessment help?

Our assessment evaluates your GenAI security posture across 6 key pillars, identifies vulnerabilities specific to your deployment, and provides a prioritized roadmap with actionable recommendations to enhance your security.

What compliance standards are covered?

The assessment covers major regulations including GDPR, CCPA, HIPAA, EU AI Act, and industry standards like ISO 27001, SOC 2, NIST AI Framework, and OWASP Top 10 for LLMs.

Is this assessment suitable for all industries?

Yes, the framework is designed to be industry-agnostic while providing specific guidance for regulated industries like healthcare, finance, and government where GenAI security is particularly critical.