As your business rapidly adopts LLMs and autonomous agents, your threat model radically shifts. We rigorously audit and stress-test your AI integrations to prevent proprietary data leakage, model poisoning, and unauthorized logic execution via prompt injection.
Testing AI isn't about running software scanners. It requires manual, adversarial manipulation of the language models your business relies on.
Comprehensive stress-testing of integrated models. We map how your custom applications interact with external APIs to uncover architectural blind spots and insecure handoffs.
We craft adversarial inputs designed to bypass your system's guardrails. By hijacking the model's instructions, we verify if an attacker can force your AI to execute unauthorized code.
Employees are actively pasting proprietary code and sensitive client data into unauthorized public chatbots. We map your network to identify, block, and manage unsanctioned AI usage.
When you fine-tune models or connect them to internal databases (RAG), you risk exposing critical IP. We test the boundaries of data retrieval, ensuring your model cannot be tricked into leaking financial records or employee PII to unauthorized users.
Transitioning from testing to policy. We draft the operational guardrails, acceptable use policies, and architectural blueprints required to safely deploy AI agents without compromising your legal standing.
Scroll to observe how a seemingly harmless chatbot integration can be reverse-engineered to dismantle your core infrastructure. The WebGL model represents your architectural data pipeline.
The attack surface begins outside your firewall. Employees copy-paste sensitive source code and client financials into public LLMs. We map these outbound data flows to identify your immediate exposure points.
You integrate an LLM to power a customer service bot. We submit adversarial text—crafted payloads designed to trick the model into ignoring its system prompt and executing our hidden commands instead.
The hijacked model is now an insider threat. Because the AI has access to internal APIs to answer user queries, we use our injected logic to force the model to query internal HR databases and spit the private data back out into the chat window.
The final failure point. If your AI is granted 'Agentic' capabilities—the ability to act, write files, or send emails—we weaponize it. We turn your helpful AI assistant into an automated vector spreading malware internally.
As mid-market businesses rush to deploy AI, architecture outpaces security. We measure the critical gaps in your infrastructure before they are actively exploited.
Of employees bypass governance to input proprietary business logic and client data into public LLMs, inadvertently training third-party models.
Of commercial AI agents currently in production fail to consistently reject adversarial prompts designed to bypass their system instructions.
Of companies implementing RAG (Retrieval-Augmented Generation) accidentally expose internal PII or financial records to unauthorized users due to poor access controls.
All engagements operate under strict confidentiality principles. SC Consulting does not publish client logos, infrastructure details, exposure findings, security architectures, or operational environments without explicit written authorization. We believe trust is earned through absolute discretion.
Organizations can reach out for strategic consultations, offensive assessments, compliance initiatives, AI security reviews, monitoring services, incident response planning, or general cybersecurity advisory.
We believe in measurable risk reduction over theoretical security. Our teams operate seamlessly alongside yours to engineer resilience that sustains the reality of modern business.