AI Security Services | LLM Testing, Governance & Forensics

What GDF delivers

AI Security services built for outcome and defensibility.

Core capabilities

LLM application threat modeling and architecture review
Prompt injection, sensitive information disclosure, and output handling tests
Retrieval, embedding, plugin, agent, and supply-chain risk assessment
AI incident response and evidence preservation planning

Our approach

Inventory sanctioned and shadow AI use cases
Map data flows, permissions, prompts, tools, models, and retrieval sources
Test against AI-specific abuse paths and sensitive data exposure
Deliver governance, control, logging, and response recommendations

Specialized practice lines

Specific capabilities within AI Security.

AI, App & Emerging Technology Security

Best for AI deployments, SaaS platforms, software companies, product teams, smart devices, firmware, and connected infrastructure.

AI security testing AI security architecture Prompt injection and data leakage review Model and dataset auditing Application security Source code review API and SaaS testing Firmware and embedded review IoT firmware and wireless testing IC and embedded cybersecurity Secure delivery review

Proof point

AI security is now a board-level issue: IBM reports that many AI-related incidents lack proper access controls, while OWASP highlights prompt injection, data disclosure, supply-chain, plugin, and excessive-agency risks.

Related services

Build a stronger program around this work.

Biometrics & Identity Sciences

GDF supports matters involving biometric systems, identity verification, fingerprint authentication, facial comparison, access-control records, and disputed identity evidence.

Critical Infrastructure Security

GDF helps utilities, water authorities, energy, transportation, manufacturing, healthcare, government, and other critical infrastructure operators assess cyber risk across IT, OT, vendors, facilities, and incident response.

OT, ICS & SCADA Security

GDF assesses OT networks, industrial control systems, SCADA environments, PLC exposure, remote engineering access, insecure protocols, vendor pathways, and incident-response readiness.

Secure AI systems before shadow AI becomes a breach, disclosure, or litigation event.