Professional Cyber Security Services
A structured review of the security controls surrounding your AI systems, from identity and data access to guardrails, logging and human oversight.
Most serious AI security failures are not caused by the model itself. They come from the architecture around it: an agent with write access to systems it only needed to read, a retrieval pipeline that ignores document permissions, an API key shared across environments, or an AI feature that can take actions without a human ever reviewing them. Prompt filters alone cannot fix these problems, because a determined attacker will eventually get past them.
The AI Control Architecture Assessment evaluates whether your AI systems are designed so that when a model is manipulated or makes a mistake, the damage is contained. We review each layer of the architecture and identify where controls are missing, misconfigured or dependent on the model behaving correctly.
How users, services and agents authenticate to AI systems, how permissions are scoped, whether agents act with their own identity or a user’s, and whether least privilege is enforced for every tool and connector.
How training data, retrieval sources, prompts and outputs are separated between users, tenants and sensitivity levels, and whether document-level permissions are enforced at retrieval time.
What actions AI systems can take, which ones require confirmation, how inputs to tools are validated and whether high-impact actions have limits that the model cannot override.
The input and output controls in place, where they sit in the architecture, how they are configured and how they fail when bypassed.
How API keys, model provider credentials and connector tokens are stored, rotated and scoped across development, testing and production.
Whether prompts, responses, tool calls and decisions are logged in a way that supports investigation, and where human review is built into the workflow.
We start with architecture documentation, data flow diagrams and interviews with the engineering and product teams that built the system. We then review configurations and code paths directly, and validate key assumptions with targeted technical tests, such as attempting cross-tenant retrieval or invoking tools outside their intended scope. Each finding is tied to an attack scenario, so it is clear why the control matters.
The assessment references OWASP Top 10 for LLM Applications, MITRE ATLAS, NIST AI RMF and ISO/IEC 42001 Annex A controls.
We identify the AI systems in scope, their users, data sources, integrations and business purpose.
We map the control layers around each system and compare them with a reference control model.
We test critical controls to confirm they work as documented.
We deliver findings, a target control architecture and a prioritized remediation plan.
Control architecture diagram for each AI system in scope
Gap analysis against a reference AI control model
Validated technical findings with attack scenarios
Target architecture and design recommendations
Prioritized remediation roadmap
This assessment suits organizations moving AI features or agents from pilot to production, teams building internal AI platforms that many business units will use, and companies preparing for customer security reviews or ISO/IEC 42001 certification.
Our risk reduction strategy melds unparalleled technical acumen with a client-focused approach to deliver targeted, cost-effective, and accessible solutions that fortify your organization against the ever- evolving cyber threat landscape.
We leverage our cybersecurity expertise to safeguard your business integrity, ensuring you operate securely, move forward confidently, and build trust in an interconnected digital world.
We deploy cutting-edge cybersecurity measures and personalized strategies to offer unwavering data protection, reinforcing our commitment to preserving your company’s invaluable digital assets.
Reach out to us today and discover the potential of bespoke cybersecurity solutions designed to reduce your business risk.