Test the AI systems you run. Design the ones you are building.
AI security consulting helps you test the systems you run and secure the ones you are building. AI Security Testing covers large language model applications, autonomous agents, tool connectors, retrieval systems, and AI red team exercises. AI Architecture & Engineering designs agent permissions, private model deployments, cloud AI controls, and secure development workflows, including systems that hold sensitive or regulated data. Start with testing when an assistant or agent is live or launching. Start with architecture when the design is still open or a model must stay inside a protected data boundary. The same principal scopes, performs, and reports the work.
Every engagement opens with your architecture diagram and a walkthrough with the builders: the model, the systems around it, the tools it can call, how identity is handled, and the data class each one touches. We agree in writing which focused services apply, the accounts to test as, usage and cost limits, who holds write access, and a contact who can pause the work.
The principal who scoped the work performs it. In testing, prompt injection, tool abuse, and data leaks are attempted by hand and recorded as transcripts with attempt counts; automated tools support the work but never write the report. In design, we read the code, the infrastructure definitions, and the live configuration, then write each control as a specific, checkable setting.
Testing leaves you findings with reproduction transcripts, fixes written for the right layer, an executive summary, and a retest report with evidence. Architecture leaves you the diagram set, a step-by-step change list your team can apply, a control mapping to the compliance frameworks you answer to, and a validation report ready for your evidence file.
Each service below has its own scope, outcomes, and a direct path to the detail.
AI Security Testing covers the AI systems you already run: the model, the application built around it, and every tool, data source, and identity the model can reach. Five focused services sit under this core. The application penetration test probes the model-facing layer and the interfaces, sessions, and access controls around it. The agentic assessment examines how an autonomous system plans, remembers, and acts on your behalf, and what it can do without a human in the loop. The connector assessment tests the components that hand the model new tools and the credentials behind them. The data boundary test checks whether retrieval ever returns information a user is not entitled to see. The red team exercise runs objective-driven campaigns against your guardrails. Every engagement follows one rhythm: scope in writing, test by hand under agreed spend and rate caps with a pause contact, report with reproduction steps and fixes, then retest with evidence.
Explore AI Security TestingLLM penetration testing for prompt injection, data disclosure, unsafe outputs, and application access controls, with reproducible findings and fixes.
From $6,000Agentic AI security assessments test tool access, memory, approval controls, and autonomous actions to identify misuse and limit potential damage.
From $8,400MCP server security testing for tool misuse, excessive permissions, and credential handling across the AI connectors you build, host, or approve.
From $3,600RAG security testing checks document access, retrieval poisoning, and permission changes, with evidence of where an AI assistant can expose data.
From $6,000AI red teaming tests prompt injection, guardrail bypasses, and multi-turn manipulation against agreed objectives, with documented results and fixes.
From $9,000AI Architecture & Engineering is design and hardening work, not testing. It covers agents that act on your behalf, models you host on your own hardware or inside a sealed network, and AI services you rent from a cloud provider. It also covers the pipeline that ships your AI features and the AI built into your own security tools. Each service produces a design or a set of applied changes with evidence, not a list of vulnerabilities; when you want the finished system attacked, our AI Security Testing core does that. We scope in writing, then review your architecture, code, and configuration by hand alongside your engineers, recording every decision and exact change with its owner named. We then confirm each change was made and package the evidence your customers, auditors, and contracts ask for. It is built for data that cannot leave: CUI, ITAR and EAR technical data, PHI, and the models, code, and trade secrets that are the product.
Explore AI Architecture & EngineeringTrust boundaries, tool permissions, approval points, and one identity per agent, designed for systems that act on your behalf.
From $6,000Model and weight integrity, inference server hardening, GPU host isolation, segmentation, and access control for on-premises, private cloud, and air-gapped deployments.
From $7,200Identity and keys, private networking, guardrails, data use and retention, and logging for the AI services you rent from a cloud provider, setting by setting.
From $3,600Design-time requirements, AI-specific threat modeling, and automated and manual gates placed in the delivery pipeline you already run, each leaving an artifact.
From $7,200AI models and agents integrated into your security monitoring, automation, and analyst workflows with least-privilege identity, human gates on write actions, and every action logged.
From $4,800We’ll help connect your objective to the right scope, people, and evidence.
