Securing AI Systems in the Enterprise
AI adoption is outpacing AI security. As enterprises deploy LLMs, copilots, and machine learning pipelines into production, they introduce a new attack surface that traditional security tooling wasn't built to see. JLYPCG helps organizations assess and secure AI systems before attackers—or careless deployment—turn them into a liability.
Updated
July 2026
Focus Areas
LLM & Prompt Injection Testing: Assessing AI applications for prompt injection, jailbreaking, and unintended data exposure. AI Supply-Chain Review: Evaluating third-party models, training data provenance, and dependency risk. Model Access Control: Reviewing who and what can query, fine-tune, or extract data from deployed models. Responsible AI Governance: Helping teams document AI risk in a form that satisfies internal audit and regulatory review.
The New Attack Surface: LLMs and Copilots
Enterprises are embedding large language models into customer service, internal tooling, and decision support faster than security teams can review them. This creates attack paths that didn't exist two years ago: prompt injection that hijacks a chatbot's instructions, data exfiltration through model responses, and jailbreaks that bypass content and access restrictions built into the application layer.
At JLYPCG, we test AI applications the way an attacker would—probing for prompt injection, insecure output handling, and cases where an LLM has been given more system access than its use case actually requires. Our findings are reported the same way as any other penetration test: reproducible, prioritized by business impact, and paired with concrete remediation steps your engineering team can act on.
We also review the surrounding architecture: how the model is authenticated, what data it can access, whether outputs are validated before triggering downstream actions, and whether logging is sufficient to detect misuse after the fact.
AI Supply-Chain & Model Risk
Most organizations deploying AI today are not training models from scratch—they are fine-tuning or calling third-party and open-source models, often with limited visibility into training data provenance, licensing, or embedded vulnerabilities. This is a supply-chain risk in the same category as any other third-party software dependency, and it deserves the same scrutiny.
We help clients inventory the AI components in their stack, assess the risk of the models and datasets they rely on, and build review processes so that adopting a new model doesn't mean adopting unknown risk. This includes checking for known model vulnerabilities, evaluating vendor security practices, and identifying where sensitive data may be exposed to third-party AI providers through everyday usage.
For clients building their own models, we review training pipelines for data poisoning risks and assess whether adversarial inputs can meaningfully degrade or manipulate model behavior in production.
Governance That Internal Audit Can Actually Use
AI governance frameworks often stay abstract—principles without a way to verify them. We help CISO, compliance, and internal audit teams turn AI risk policy into something concrete: an inventory of where AI is used, documented risk assessments for each use case, and evidence that access controls and monitoring are actually in place, not just written down.
This matters most at the moment it's tested—when a regulator asks how an AI decision was made, when a client asks how their data is used by your AI tools, or when an incident forces you to explain what happened. Our goal is that when that moment comes, you have an answer ready, backed by documentation your team can stand behind.
