The questions that actually matter inside an enterprise AI rollout are not strategic. They are concrete, technical, and unforgiving.
Prompt injection in tool-calling agents. RAG poisoning through user-controllable context. Agent identity propagation across service boundaries. MCP server trust boundaries. Cross-tenant isolation in vector stores and embedding databases. Data lineage from prompt to response. LLM gateway controls. Shadow AI discovery across the SaaS estate. Plugin and tool-call authorisation. Model supply-chain risk.
Every Secucloud engagement is grounded in these specifics — and in the cloud-native architecture, identity propagation, segmentation, and assurance mapping that surround them. Strategic AI security advice without this technical floor is just opinion.
Every Secucloud engagement is grounded in the same intellectual scaffolding: the Secucloud AI Security Framework. Six domains, five maturity levels, and an explicit mapping to NIST AI RMF, ISO/IEC 42001, the EU AI Act, and the OWASP LLM Top 10.
The framework is what turns subjective findings into a maturity score a board can read, an auditor can verify, and an engineering team can act on. It is also why every engagement produces an output that survives the audit cycle.
Explore the Framework in Full →A structured engagement covering all six SAISF domains, producing a board-ready map of where you are, where you need to be, and what stands between.
Most organisations have AI activity already underway — sanctioned tools, shadow Copilots, vendor-embedded features — without a coherent view of the risk it carries. The Readiness Assessment establishes that view. Discovery interviews, document review, technical sampling, and gap analysis produce a maturity score per domain, a prioritised remediation roadmap, and a one-page board summary.
A focused one-to-three week review of a specific AI deployment — Copilot for M365, Bedrock, Azure OpenAI, custom RAG, or agent frameworks — assessed against modern threat models.
Where the Readiness Assessment surveys the landscape, the Architecture Review goes deep on a single system. Identity model, data boundaries, prompt injection resistance, tool-call security, network egress, monitoring strategy. Output is a written architecture critique, threat model, and remediation backlog ready for engineering teams to execute against.
A half-day executive simulation putting your board, audit committee, and finance leadership through three AI-era incident scenarios their current playbooks were never written for.
Voice-cloned CEOs authorising fraudulent payments. Deepfake video calls in the middle of a Teams meeting. AI-generated spear phishing that bypasses every awareness control you have. The tabletop is structured around three scenarios drawn from real recent incidents, run by a facilitator who has lived the cloud security side. Output is a debrief, a gap report, and three to five concrete playbook changes.
A 30-minute discovery call to confirm fit, scope, and timeline. No obligation. No proposal until both sides agree the engagement is the right one.
Document review, stakeholder interviews, and technical sampling. We learn your environment before we offer opinions on it.
Findings are mapped to the SAISF framework and underlying standards. Recommendations are prioritised by risk reduction per pound spent.
A written report, a board-ready summary, and a debrief session. The deliverables are designed to outlive the engagement and survive a regulator's eye.
You cannot secure AI workloads without first securing the cloud they run on. Secucloud's AI specialism is built on years of cloud security architecture work across regulated industries — financial services, public sector, healthcare.
When AI engagements surface underlying cloud security gaps, we are equipped to address them as part of the same conversation rather than handing you to another supplier.
A 30-minute scoping call. No pitch, no proposal until we both agree the engagement is right. The fastest way to decide is to talk.