AI governance and risk: inventory AI use, classify sensitive data, identify model threats, define human oversight and controls against leakage, misuse and drift.
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Security technology · Engineering · Security assessments
Your AI initiative needs clear ownership of data, decisions and operational risk.
If your teams are introducing generative AI, autonomous workflows or model-assisted analysis, we assess where the technology may act, what it can access and how its output will be challenged.
Engineering, evidence and operational results
See the work behind the offer
Follow a realistic engineering situation, from the first signal to the decision and the evidence handed over.
DEEP INFOSEC
What this means for your organisation
If your teams are introducing generative AI, autonomous workflows or model-assisted analysis, we assess where the technology may act, what it can access and how its output will be challenged. Security, privacy, model performance and human authorization are considered together.
You receive a defined AI use-case boundary, a threat and control assessment, evaluation scenarios, access and logging rules, and an implementation plan. We also help teams integrate useful AI capabilities into existing security and business processes.
A practical starting point
Choose a starting scope, review the evidence and prepare a usable purchasing brief. Public service information is open to everyone; confidential architecture and qualification materials follow the secure dossier process.
Prepare a project brief ↗Selected readings ↗The challenges we solve
Specialist workstreams
Govern AI adoption, assess model and workflow risks, build human-verifiable decision support and controlled integrations.
Secure AI engineering: evaluate retrieval-augmented generation, agent permissions, prompt-injection defenses, isolation, model supply chain and audit logging.
Applied decision intelligence: integrate explainable analytics with incident, operations, engineering or procurement workflows using deterministic controls as the primary authorization layer.
AI assurance and evaluation: benchmark quality, robustness, privacy, bias relevant to application, traceability and acceptance before scaling.
What your team receives
What your team receives
- AI use-case inventory, data-flow and threat-model register
- Secure model/RAG/agent reference architecture and control matrix
- Evaluation protocol with representative tasks, failures and human-review thresholds
- Governance, monitoring and incident-response playbook
Defined entry offers
Acceptance is tied to the agreed deliverables and documented review.
01
Technical assessment
AI governance and risk: inventory AI use, classify sensitive data, identify model threats, define human oversight and controls against leakage, misuse and drift.
AI use-case inventory, data-flow and threat-model registerRequest a technical and commercial proposal ↗02
Design and controlled evaluation
Secure AI engineering: evaluate retrieval-augmented generation, agent permissions, prompt-injection defenses, isolation, model supply chain and audit logging.
Secure model/RAG/agent reference architecture and control matrixRequest a technical and commercial proposal ↗03
Delivery, review & continuity
AI assurance and evaluation: benchmark quality, robustness, privacy, bias relevant to application, traceability and acceptance before scaling.
Governance, monitoring and incident-response playbookRequest a technical and commercial proposal ↗Scope & architecture
How a project progresses
Advisory, engineering prototype or bounded production integration; automation authority remains explicitly controlled and model-provider terms are selected by the client.
- 01
Discover and define: assets, objectives, dependencies, data access, stakeholders and boundaries.
- 02
Design and validate: target architecture, methods, constraints, workplan and objective acceptance criteria.
- 03
Deliver and transfer: approved implementation, findings, tests, documentation, training and next-stage options.
Request a technical and commercial proposal
Identify one high-value workflow, permitted data, failure consequences and a measurable evaluation dataset.
Engineering and services that work together
What changes for your organization
Where this matters
AI can help security and operations teams reason through complex evidence, but it also changes data flows, access boundaries and accountability. We support secure model adoption, evaluation, human oversight and workflow integration. The design keeps sensitive data handling explicit and ensures that decisions, exceptions and actions remain explainable to the people responsible for them.
Solutions for your role
Begin with your decisions, risks and procurement obligations—not a generic product catalogue.
Connected systems, reliable information and locally operable infrastructure
University research and applied engineering network
Our work with university and research teams connects technical challenges to reproducible methods, experiments, professional learning and measurable operational use. Collaboration can involve attack-path modelling, AI security, post-quantum preparation, environmental and energy measurement, educational technology and sovereign data systems. Research, delivery and assessment responsibilities are defined for each programme.
Your project receives a research protocol, named workstreams and governance, technical outputs, validation criteria and a path from prototype to supported operations.
University research and applied engineering network ↗