Generative AI Security Consulting

Overview

Generative AI adoption is rarely a single decision. It's a spectrum, and the right security controls depend on where an organization actually sits on it. We help organizations identify their stage of GenAI adoption and apply controls appropriate to that stage, rather than a one-size-fits-all AI policy.

Our Scope: Infrastructure Security, Not Model Security

AI architecture has two distinct layers, and it helps to be explicit about which one we work in.

Model architecture is the internal design of the AI model itself: its neural network layers, training data, and optimization techniques. That responsibility sits with your data science and machine learning teams, or with the model vendor. It is not something we do.

Infrastructure architecture (sometimes called services architecture) is how your organization exposes and consumes AI capabilities: the API gateways, load balancers, caching layers, identity controls, and monitoring that sit between your users and the model. This is where we work.

The same questions apply whether your AI workloads run on infrastructure you manage (IaaS), a managed platform (PaaS), or a fully managed AI service (SaaS): who can reach your API endpoints, what data can leak through requests and responses, how access is authenticated and rate-limited, and what gets logged when something goes wrong. See our detailed breakdown of this layer in Defense in Depth for LLM Applications.

Three categories of GenAI exposure we assess

1. Consumer-facing GenAI use: employees using public tools (e.g., Claude, ChatGPT, Gemini, Perplexity). Risk centers on data leakage, lack of usage policy, and unmanaged exposure of sensitive corporate information.
2. Enterprise AI application security: GenAI features embedded in SaaS platforms your organization already uses (e.g., Microsoft Copilot, Salesforce AI features). Risk centers on shadow AI, integration security, and access governance.
3. Custom AI infrastructure and agent security: organizations building on platforms such as Amazon Bedrock, Azure AI Foundry, or Google Vertex AI. Risk centers on secure API integration, endpoint exposure, secure data pipelines, AI-agent boundary enforcement, and runtime protection.

Our framework: Observability → Governance → Agent Boundaries → Runtime Protection

We apply a four-stage progression across all three categories, recommending specific technical controls based on what your environment and risk profile actually require, from usage policy and DLP to LLM gateways, CNAPP integration, and runtime monitoring. The framework is vendor-agnostic: no recommendation is tied to a single product line.

An LLM Gateway is one of the key controls at the Runtime Protection stage, giving you a central point to inspect prompts and tool calls before they reach a model or an external service. See our breakdown of how that fits alongside your existing firewalls, WAFs, and identity controls in Defense in Depth for LLM Applications.

At the Agent Boundaries stage, we also assess execution sandboxing, isolating the code an agent runs and the actions it takes in a disposable, restricted environment so that a manipulated agent cannot reach beyond the task it was given. This contains the damage even when a prompt injection or a poisoned tool gets past inspection.

What we help you guard against

Analysts are increasingly explicit about the stakes of getting AI governance wrong: Gartner predicts that by 2028, misconfigured AI in cyber-physical systems will shut down national critical infrastructure in a G20 country. The risks specific to adopting AI agents in the enterprise are different, but equally well documented:

• Prompt injection: malicious instructions embedded in user input or retrieved content that alter an LLM's intended behavior, ranked the top risk in the OWASP Top 10 for LLM Applications (LLM01:2025).
• MCP tool poisoning: malicious instructions embedded in a Model Context Protocol tool's metadata or responses, exploiting the gap between how tools are reviewed when an agent connects and how their output is trusted at runtime.
• Memory poisoning: an attacker writes malicious content into an agent's persistent memory so it acts on it in future sessions, long after the original interaction ends (tracked as ASI06 in the OWASP Top 10 for Agentic Applications).
• Supply chain vulnerabilities: risk introduced through foundation models, hosted APIs, fine-tuned models, RAG data sources, and MCP tools sourced from third parties (OWASP Top 10 for LLM Applications, LLM03:2025).

This is exactly what our four-stage framework above is built to address, from observability that surfaces anomalous tool and memory behavior, to agent boundaries and runtime protection that contain it. See the full breakdown in our Insights article on securing agentic AI adoption.

What you get

A clear picture of where your organization sits on the GenAI adoption spectrum, the specific risks tied to your actual use cases, and a practical set of controls for data protection, governance, AI-agent boundaries, and responsible deployment.