Identity and access
Human identities, service accounts, workload identity, role design, privilege escalation, temporary access, break-glass access, and access reviews.
Cloud Security
Make cloud authority visible.
Review identities, workloads, secrets, pipelines, environments, and AI infrastructure before drift becomes exposure.
Turn cloud complexity into clear control decisions.
System map
The surface is mapped before the work begins.
Each Solutions page uses the same operating view: define the trust surface, identify the review loop, and make the evidence usable for builders and leaders.
Cloud Security
Trust map
Identity
Workloads
Secrets
Pipelines
AI data
Logs
Review loop
Frame
Map
Inspect
Evidence
Context
The cloud control plane is part of the product.
Cloud exposure is not limited to public storage or open network ports.
Identity design, deployment pipelines, service permissions, secrets, environment boundaries, workload configuration, logging, and infrastructure code all shape what the product can safely do.
AI systems add further complexity through model endpoints, vector stores, datasets, agent runtimes, accelerated compute, external providers, and automated tool access.
What SecureSpace examines
Workloads
Containers, serverless, virtual machines, managed services, runtime permissions, network exposure, isolation, patching, and image posture.
Secrets
Storage, distribution, rotation, CI/CD exposure, developer access, application use, and logging risk.
Environments
Development, testing, staging, production, data separation, shared services, promotion paths, and administrative access.
CI/CD and delivery
Build permissions, pipeline identity, artifact integrity, deployment approval, branch protection, secrets, third-party actions, and rollback.
Infrastructure as code
Review process, state handling, drift, permission design, reusable modules, destructive changes, and policy enforcement.
Logging and response
Cloud audit logs, workload logs, identity events, retention, alert ownership, incident reconstruction, and evidence quality.
AI and data infrastructure
Model endpoints, datasets, retrieval stores, vector databases, agent runtimes, tool credentials, external model providers, and data movement.
Common engagement triggers
01
Preparing a production launch
02
Moving into enterprise markets
03
Expanding cloud permissions
04
Introducing AI workloads
05
Reworking CI/CD
06
Investigating secret exposure
07
Reviewing a cloud migration
08
Preparing for buyer diligence
09
Improving environment separation
10
Responding to a security event
How SecureSpace approaches the work
01
Frame cloud ownership
Identify accounts, projects, environments, owners, boundaries, and the systems that depend on them.
02
Map identity and data flow
Trace where humans, services, workloads, pipelines, and AI systems receive authority.
03
Review critical configuration
Inspect representative cloud, workload, pipeline, secret, and environment settings within scope.
04
Model cloud failure paths
Examine how compromise, misconfiguration, credential exposure, or pipeline abuse could move through the system.
05
Prioritise remediation
Separate immediate exposure from maturity work, documentation gaps, and future architecture decisions.
06
Create useful evidence
Produce records that help engineering, security, leadership, and buyer-facing conversations.
What the work can produce
Cloud architecture map
Identity and permission findings
Workload-security findings
Secrets analysis
Environment-separation review
CI/CD review
Infrastructure-as-code observations
Logging and evidence recommendations
AI-infrastructure risk review
Prioritised remediation plan
Who it is for
Teams that need clarity without slowing the build.
Teams scaling infrastructure
Platform teams maintaining delivery pipelines
AI teams operating retrieval and model infrastructure
Companies preparing for buyer diligence
Security teams reviewing cloud authority
Mintos AI
Cloud context shapes the Mintos AI control plane.
AI-connected systems frequently inherit authority from cloud identity, workload, and delivery systems.
SecureSpace cloud work helps identify the infrastructure patterns Mintos AI may need to understand over time.
What this work should not overclaim
SecureSpace should not describe this service as an always-on cloud oversight capability unless that capability has actually been implemented and contracted.
A point-in-time cloud review reflects the agreed scope and the state visible during the engagement.
Cloud findings depend on available access, provider data, environment maturity, and the ability to inspect relevant systems.
Questions teams usually ask
Which cloud providers do you support?
Scope is discussed individually. SecureSpace can review common cloud patterns, but provider-specific access and expertise should be confirmed before work begins.
Do you need administrative access?
Not necessarily. Least-privilege access is preferred. The required access depends on the scope and expected evidence.
Can you review infrastructure as code?
Yes. Infrastructure as code can be reviewed for permissions, state handling, environment separation, destructive changes, and policy gaps.
Can you review CI/CD?
Yes. Pipeline identities, secrets, branch controls, artifact integrity, and deployment paths can be included.
Do you review AI infrastructure?
Yes, where it is within scope. That can include retrieval stores, model endpoints, agent runtimes, data movement, and tool credentials.
Is this a compliance audit?
No. It can support readiness discussions, but formal compliance or certification requires the right independent process.
Continue from here
Next step
Start with the system, not the category label.
Tell us what you are building, which decision is becoming difficult, and where the security boundary feels unclear.