Securing Generative AI in Regulated Industries
GenAI systems introduce an entirely new class of security risks that traditional controls cannot address. SOAISEC Labs provides full-stack GenAI security — from threat intelligence and red teaming to unified guardrails across browser, IDE, API, and endpoint/runtime, and supply chain scanning.
GenAI Threat Landscape
Six attack categories that security teams in regulated industries must defend against when deploying Generative AI.
Prompt Injection
Attackers embed malicious instructions inside user input or external documents to hijack model behavior, override system prompts, and exfiltrate data through seemingly normal interactions.
Jailbreaking
Adversarial techniques that convince models to bypass safety guardrails — including role-play attacks and multi-turn manipulation — causing them to produce harmful or policy-violating outputs.
RAG Poisoning
Malicious actors inject crafted content into retrieval-augmented generation knowledge bases, causing the model to retrieve and act on corrupted context — leading to misinformation or sabotaged decisions.
Model Theft
Systematic extraction of a proprietary model's weights, decision boundaries, or training data through carefully crafted queries — enabling IP theft and creating clone models that bypass licensing controls.
Data Exfiltration
Models trained on or exposed to sensitive data can inadvertently reveal PII, trade secrets, or regulated information through prompt extraction and membership inference attacks.
Hallucination Exploitation
Deliberate exploitation of a model's tendency to confabulate — forcing it to produce false citations, fabricated regulations, or plausible but incorrect legal or financial guidance at scale.
How SentraSuite Addresses GenAI Threats
Purpose-built modules for every layer of your GenAI security stack — from pre-deployment scanning to real-time runtime control.
Stress-tests your GenAI systems with curated adversarial scenarios — prompt injection chains, jailbreak libraries, RAG corruption tests — producing reproducible findings for your security and model risk teams.
Unifies GenAI security and observability across browser, IDE, API, and endpoint/runtime surfaces through a centralized, API-first, CPU-only backend — enforcing DLP, prompt injection detection, jailbreak prevention, and file moderation everywhere AI is used.