Secure AI Application 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.
01Prompt 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. Traditional WAFs cannot detect these semantic attacks.
02Data 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. Once data is generated by the model, it's difficult to 'unlearn' or redact it reliably.
03Supply 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 (Pickle files), or exploit vulnerabilities in third-party plugins to compromise your entire AI infrastructure.
04Non-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 that includes evaluating factual accuracy, toxicity, and bias at scale.
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. We evaluate your model's resilience against manipulation and its adherence to safety guidelines.
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, preventing 'context leakage' between tenants or user roles.
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 before they reach your users, ensuring brand safety.
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 to prevent an agent from being tricked into performing destructive actions via indirect prompt injection.
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 to prevent poisoning attacks.
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 for transparency, risk management, and data governance.
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.
Building with LLMs?
Don't let security block your innovation. Let us help you ship secure AI applications faster.
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