Secure AI Development
Ship AI with confidence. We help you build, test, and deploy secure LLM applications, protecting against prompt injection, data leakage, and model theft.
Why AI Security Is Different
Traditional application security isn't enough. Generative AI introduces probabilistic risks that standard firewalls and scanners miss.
Prompt Injection & Jailbreaks
LLMs are susceptible to adversarial inputs that can bypass safety filters and hijack model behavior. Attackers can use techniques like 'DAN' (Do Anything Now), role-playing attacks, or foreign language encoding to force the model to generate harmful content, execute unauthorized commands, or reveal its system instructions.
Data Leakage & Privacy
Generative AI models can inadvertently memorize and regurgitate sensitive information found in their training data or context window. This creates a significant risk of PII exposure, leakage of trade secrets, or accidental disclosure of proprietary codebases.
Supply Chain Vulnerabilities
Modern AI stacks rely heavily on open-source models (Hugging Face), vector databases, and orchestration frameworks (LangChain). Malicious actors can poison these dependencies, inject backdoors into model weights, or exploit vulnerabilities in third-party plugins.
Non-Deterministic Output & Hallucinations
Unlike traditional deterministic software, AI behavior is probabilistic. Models can confidently generate false information (hallucinations) or behave inconsistently under load. Ensuring consistent, safe, and reliable outputs requires a new paradigm of testing.
Our AI Security Capabilities
From red teaming foundation models to securing RAG pipelines, we cover the entire AI lifecycle.
AI Red Teaming
We conduct adversarial simulation to stress-test your models against real-world attacks. Our team attempts advanced prompt injections, jailbreaks, and extraction attacks to find weaknesses before you deploy.
Secure RAG Architecture
We design and review Retrieval-Augmented Generation systems to prevent unauthorized data access. We ensure your vector databases and retrieval logic implement strict access controls (RBAC) so users only retrieve documents they are authorized to see.
LLM Guardrails Implementation
We develop robust input/output filtering layers to sanitize interactions. Using frameworks like NeMo Guardrails or custom classifiers, we block malicious prompts before they reach your model and filter out toxic or unsafe responses.
Agentic AI Security
Autonomous agents with tool access pose high risks. We secure your agent execution environments by implementing strict permission boundaries, human-in-the-loop verification for critical actions, and sandboxing.
Model Supply Chain Review
We perform deep vulnerability scanning for your AI artifacts. This includes scanning model files for malicious code, analyzing dependencies for known vulnerabilities, and verifying the integrity of your training datasets.
Compliance & Governance
We help you align with emerging global AI standards. We prepare your systems for the EU AI Act, NIST AI Risk Management Framework (AI RMF), and ISO 42001, ensuring you meet regulatory requirements.
How We Secure Your AI
A structured, risk-based approach to AI adoption. We move from threat modeling to continuous monitoring.
Threat Modeling
We analyze your specific AI use case to identify unique attack surfaces, from data ingestion to model output.
Architecture Review
We assess your RAG pipelines, vector stores, and API integrations for design flaws and access control issues.
Adversarial Testing
Our red team executes targeted campaigns using automated fuzzing and manual expertise to bypass your guardrails.
Remediation & Hardening
We provide code-level fixes, prompt engineering adjustments, and architectural changes to close security gaps.
AI security wins
How we helped teams ship secure LLM applications with confidence.
Prompt Injection Attack PreventedZecurX's red team bypassed our chatbot's safety filters using multi-step DAN attacks. They then helped us implement NeMo Guardrails that blocked 99.7% of adversarial inputs.
%
Attacks Blocked
Post-guardrails implementation
Jailbreaks Found
During red team exercise
RAG Data Isolation EnforcedOur RAG pipeline was leaking documents across tenant boundaries. ZecurX redesigned our vector DB access layer with proper RBAC, preventing cross-tenant data exposure.
Data Leaks
Post-remediation
x
Faster Compliance
For enterprise onboarding
AI Agent Safety Boundaries SetOur autonomous agent had unrestricted tool access. ZecurX implemented sandboxing and human-in-the-loop verification that prevented the agent from executing destructive operations.
%
Critical Actions Gated
Human-in-the-loop
Escape Paths Closed
Agent sandbox hardened
Building with LLMs?
Don't let security block your innovation. Let us help you ship secure AI applications faster.