4. Security & Compliance

Automates continuous security testing of AI systems through CI/CD integration to identify vulnerabilities and ensure compliance.

Tests for prompt injection vulnerabilities in LLM applications using simulated attacks and payload crafting techniques.

Enables implementation of security compliance frameworks and governance structures to ensure adherence to regulatory standards.

Enforces AI agent safety through content filtering, guardrails, and compliance monitoring to prevent harmful outputs and ensure regulatory adherence.

Provides backend authentication and authorization patterns including JWT, OAuth2, session management, and RBAC for secure token handling.

Executes model inversion attacks to extract training data and sensitive information from AI models, revealing privacy risks.

Secures AI/ML infrastructure components including model storage, API endpoints, and compute resources.

Enables ethical vulnerability reporting, coordinated disclosure, and bug bounty participation for AI system security.

Implements LLM security measures including input filtering, output guarding, and defensive prompting to prevent vulnerabilities.

Secures backend applications against OWASP threats through authentication, encryption, scanning, and compliance procedures.

Exploits AI system vulnerabilities using advanced prompt manipulation techniques including direct attacks and multi-turn exploitation.

Provides AI red teaming frameworks and tools (PyRIT, garak, Counterfit) for automated security testing and vulnerability assessment of AI systems.

Generates professional security reports with executive summaries, documented findings, and remediation tracking for red team engagements.

Tests and evaluates AI safety filters, content moderation systems, and guardrails through controlled bypass techniques for security assessment.

Automates security testing for AI/ML systems with CI/CD integration to ensure continuous security validation in development pipelines.

Systematic vulnerability discovery, threat modeling, and attack surface analysis for AI/LLM security assessments.

Provides structured AI security testing methodologies including threat modeling, penetration testing, and red team operations to identify system vulnerabilities.

Explores attack vectors and techniques for compromising Retrieval-Augmented Generation (RAG) systems, emphasizing knowledge base poisoning vulnerabilities.

Enables secure access to ArcGIS services and content using OAuth 2.0, API keys, and identity management for authenticated users.

Provides security best practices for deploying AI/ML models to production, ensuring secure and compliant deployments.

Tests AI systems for code injection vulnerabilities, including prompt-to-code attacks and agent exploitation, to identify security weaknesses.

Provides advanced techniques for circumventing safety mechanisms and constraints in large language models.

Conducts OWASP Top 10 security audits to detect SQL injection, XSS, CSRF, and input sanitization flaws while validating authentication and authorization mechanisms.

Engineering security and privacy features into software systems, ensuring data protection and regulatory compliance.