AI Security
Overview
An assessment of how your company builds and uses AI models and what risks come with them. We look at how data enters your models, how outputs are used, and how responsibilities and decisions around AI are handled across teams.
This makes AI usage predictable and defensible in your company.
AI/ML Risk & Governance
A review of how you build, use, and manage AI and ML models in your company. We look at how data flows into models, how outputs are used, and how decisions are made around risk, accuracy, and responsibility.
The goal is to make sure your AI use is safe, explainable, and matches your way of working.
The Process
-
Understand how you use AI
We talk to your teams that build or rely on AI and review your data sources, model pipelines, and how AI outputs influence product behavior or internal decisions. This gives us a solid picture of where AI sits in your company and how critical it is.
We focus on understanding actual usage, not theoretical intentions, so the governance model fits your requirements.
-
Identify risks and weak controls
We look at where your AI processes introduce exposure, for example: unclear ownership, unsafe data flows, missing validation, unmonitored model behavior, or dependencies on external AI services with limited transparency.
The aim is to highlight risks that affect your customers, compliance obligations, or internal decision-making.
-
Define practical governance
We help you establish simple, workable rules for ownership, approvals, documentation, and monitoring. This includes defining who is responsible for model decisions, how changes get evaluated, and how model behavior should be tracked over time.
We give you clear expectations for how prompts, data, and model outputs should be managed.
The result is a governance model you can actually follow.
The Outcomes
-
Clear visibility into how AI is used across your company
-
A practical governance model with ownership and expectations
-
A risk overview tied to your company
AI/ML Security Engineering
A hands-on review of the systems and pipelines that train, deploy, and operate your AI models.
We make sure your AI workloads don’t introduce new security gaps, expose sensitive data, or become an easy target for attackers.
The Process
-
Analyze your AI pipeline
We review how data enters your pipeline, how models are trained, validated, and deployed, and how they interact with your existing infrastructure and external AI services. We also review prompt construction and handling to check whether prompts or system instructions could be manipulated or cause unintended behavior. Additionally, we check whether model execution, hosting, and inference endpoints are properly isolated and protected.
It shows where security boundaries are strong, where they are unclear, and where you need additional safeguards.
-
Find weaknesses
We identify parts of the pipeline that introduce risk, such as unclear access boundaries, unsafe deployment practices, inconsistent environments, or endpoints that expose more than intended.
We also look at how external AI services are integrated and whether those connections could be misused.
We concentrate on weaknesses that would create real impact on your company, focused on issues that matter in practice. -
Strengthen the setup
We work with your engineers to add controls that integrate smoothly into your workflow. These improvements may include safer deployment patterns, clearer isolation between environments, more predictable access boundaries, and monitoring that highlights meaningful issues without adding noise.
The result is a pipeline that supports fast iteration while keeping your AI workloads secure.
The Outcomes
-
A secure AI/ML pipeline from training to production
-
Protection for data used by and exposed through your models
-
Safe model integrations, including MCP and tool-calling boundaries