Security & data control
Your company should control the data.Not the AI model.
TEMRIK places a secure AI orchestration and human-control layer between business information and the AI models used to process it. Keep company knowledge, permissions, workflows and evidence under organisational control. Give models only the context required for the task.
The model can change. The company's operating memory should not have to.
Encryption · tenant isolation · scoped model access · auditability · human authority
Company controlled
Business context
- Business data
- Documents
- Policies
- Playbooks
- Permissions
- Decision history
TEMRIK control layer
Policy + context
- Tenant boundary
- Identity
- Policy
- Context selection
- Model router
- Dispatcher Gate
- Audit record
Replaceable compute
Selected model
- OpenAI
- Anthropic
- Gemini
- Azure-hosted
- AWS-hosted
- Private / open
- Confidential endpoint
Step 1
Model result
Step 2
TEMRIK policy check
Step 3
Authorised action
Provider names illustrate architectural choice. Availability depends on deployment configuration and does not imply every provider is currently integrated.
The AI security question
The risk is not just where your files are stored.
Once AI can reason across email, accounting, contracts, CRM, projects and internal knowledge, cybersecurity becomes an orchestration problem.
AI security is no longer only about securing the database.It is about securing the path from information to action.
Company data plane
Keep the intelligence of the business outside the model.
The durable asset is not a chat transcript with a foundation model. It is the organisation's source documents, structured knowledge, workflow state, permissions, business rules, playbooks, evidence, decisions and approved outcomes.
Company data
Owned business information remains the source of truth.
Permission evaluation
Identity, tenant and role determine what may be retrieved.
Minimum necessary context
Only the context required for the task should be assembled.
Selected model
A configured compute environment performs the defined reasoning task.
Policy + authority
The result is evaluated against rules outside the model.
Recorded outcome
Approved actions and evidence remain part of the company system.
Model portability
Today's best model may not be tomorrow's best model.
Model-independent AI orchestration is designed to keep company workflows and policy separate from a single model vendor. Different workloads can justify different models, privacy controls, regions, costs and performance profiles.
TEMRIK model router
Model A
Selected by workload policy
Model B
Selected by workload policy
Private model
Selected by workload policy
Future model
Selected by workload policy
Model choice
Use different compute for different workloads.
Model replacement
Move a workflow when capability, price or privacy changes.
Reduced lock-in
Keep rules and company knowledge outside provider-specific prompts where practical.
Workload routing
Match the security boundary to the sensitivity of the work.
Resilience
Avoid treating one provider as the entire operating architecture.
Context control
The safest document is sometimes the document the model never receives.
Least-privilege AI begins before encryption. TEMRIK's design direction is to identify the user, workflow and permissions first, retrieve only relevant sources, then assemble the minimum useful context.
Minimum necessary context
A subcontractor-variation workflow may need the relevant contract clause, instruction, drawing revision, claimed amount and programme impact. It probably does not need payroll, unrelated legal files, every company email, unrelated projects or HR records.
Confidential AI
Protect data while it is stored. Protect it while it travels. Then protect it while it computes.
At rest
Information sitting in databases, object storage, indexes and backups.
- Encrypted storage
- Access control
- Tenant boundaries
- Key-management options
In transit
Information moving between users, TEMRIK, integrations and approved AI services.
- Encrypted connections
- Authenticated services
- Controlled endpoints
- Transport security
In use
Information actively being processed by CPUs and GPUs.
- Trusted Execution Environments
- Confidential virtual machines
- Encrypted compute memory
- Hardware attestation
The direction of travel
Meta Private Processing · public reference architecture
Private AI computation is moving from theory into infrastructure.
Meta has publicly described Private Processing as confidential-computing infrastructure for AI workloads, using confidential virtual machines, hardware-backed memory protection and remote attestation. Its published architecture demonstrates one emerging model for protecting cloud AI data while it is actively processed.
TEMRIK is not affiliated with Meta and does not claim to use Meta infrastructure. The example is included to show where the wider confidential-computing industry is heading.
Read Meta's published architecturePublicly documented design elements
Choose the compute boundary
Different workloads can justify different AI environments.
Major enterprise AI providers offer different combinations of no-training commitments, retention controls, customer-managed keys, private networking, geography controls and confidential-compute features. TEMRIK's role is to make those provider choices part of policy rather than invisible implementation detail.
Standard enterprise AI
Approved provider, scoped context, encrypted transport.
Zero-retention AI
Eligible services configured to minimise or eliminate provider retention.
Customer-cloud AI
Customer-controlled cloud resources, identity and networking where supported.
Private model
Dedicated or customer-hosted model endpoint for selected workloads.
Confidential inference
Hardware-attested TEE/CVM boundary for data in use.
Clear language
Encryption, zero retention and confidential processing are different controls.
Encryption in transit
Protects data while travelling across networks.
Zero data retention
Addresses whether eligible request content is retained after processing.
Customer-managed keys
Give customers additional control over keys for supported stored data.
Confidential computing
Protects data in use inside a hardware-enforced trusted environment.
True end-to-end encryption
Requires plaintext to remain unavailable to intermediaries outside the defined trusted endpoints.
TEMRIK does not describe an ordinary commercial LLM API as “end-to-end encrypted” merely because TLS is used. Precise language matters because each control addresses a different part of the risk.
Secure the control plane
The model is only one component of the risk.
The control plane determines what the model may know, which tools it may use, and whether a generated recommendation can become a real-world action.
Identity
Who is asking?
Tenant
Which organisation owns the context?
Role
What is this person or agent permitted to access?
Context
Which information is actually relevant?
Model
Which compute environment is authorised?
Tools
What systems may the model call?
Action
What may it prepare or propose?
Authority
Who must approve consequential release?
Audit
What evidence should remain afterwards?
Agents need boundaries
An AI that can act needs more controls than an AI that can answer.
OWASP's current GenAI security work highlights prompt injection, sensitive-information disclosure, supply-chain risk, improper output handling and excessive agency. These are orchestration problems as much as model problems.
The AI does not define its own permissions
Untrusted input
Treat instructions and external content as data, not authority.
TEMRIK context control
Select sources through identity, tenant and workflow rules.
Model
Generate analysis or a proposed action.
Untrusted output
A confident answer is still not an authorisation token.
TEMRIK policy control
Evaluate tool access, scope and release conditions deterministically.
Human authority
Require an authorised person where the consequence demands it.
Action + audit
Release only the approved scope and retain the evidence.
The LLM should never be the security policy.
One controlled stack
The model sits inside the operating architecture. The operating architecture does not sit inside the model.
Layer 7
Auditable action
Decision · evidence · actor · time · outcome
Layer 6
Human control
Dispatcher Gate · approval · escalation
Layer 5
Agent + tool policy
Allowed tools · scope · limits · exceptions
Layer 4
Model router
Selected LLM · provider policy · private inference
Layer 3
TEMRIK knowledge + playbooks
Retrieval · rules · process · evidence
Layer 2
Identity + tenant boundary
Authentication · organisation · role · permission
Layer 1
Company data
Documents · systems · records · knowledge
Change the model
Model-centric architecture
The business gets built around one provider.
Company-centric architecture
The provider becomes a replaceable compute component.
Change the model. Keep the business.
Security modes
Apply stronger boundaries as the sensitivity and consequence increase.
Standard controlled AI
- Tenant controls
- Scoped context
- Enterprise provider
- Encrypted transport
- Audit history
- Human authority
Restricted data workflow
- Narrower context
- Retention controls
- Regional requirements
- Stricter tool permissions
Private cloud / customer environment
- Customer cloud resources
- Private networking
- Customer-managed keys
- Enterprise identity
Confidential inference
- Hardware TEE
- Attestation
- Encrypted memory
- Ephemeral processing
Exact controls depend on customer requirements, provider capabilities and the agreed implementation architecture.
Security can begin before encryption
What TEMRIK should not send matters as much as how it is encrypted.
01
Do not send 30 documents when two paragraphs are sufficient.
02
Do not expose HR records to a construction variation workflow.
03
Do not expose every project to a user authorised for one project.
04
Do not put passwords or API credentials into system prompts.
05
Do not give an AI payment authority merely because it can calculate an invoice.
06
Do not treat generated output as proof of authority.
07
Do not let an external document silently redefine an agent's permissions.
08
Do not connect a tool without defining what the tool is allowed to do.
Human control remains
Security controls what AI can reach. Human control decides what AI can release.
TEMRIK's existing human-control architecture separates preparation from permission. Data access, model access, tools and proposed actions can all be controlled before a consequential external action is released.
Questions worth asking
Bring your current AI architecture. We'll start with the boundary.
Security by workflow
Not every workflow needs the same boundary.
A public FAQ assistant does not necessarily need the same security environment as legal analysis, financial records, board material, employee information, confidential project information, intellectual property or acquisition documents.
Data sensitivity × action consequence × regulatory requirement × model requirement
Low sensitivity / low consequence
Standard controlled AI may be sufficient.
Low sensitivity / high consequence
Stronger action controls and explicit authority.
High sensitivity / low consequence
Narrow context, retention and provider controls.
High sensitivity / high consequence
Strongest available data and action boundary; consider private or confidential compute.
TEMRIK security principles
The company owns the context.
The model receives only what it needs.
The model is replaceable.
Permissions live outside the prompt.
Generating is not authorising.
Tools have defined scope.
Higher-risk work requires stronger boundaries.
Important actions remain auditable.
Humans retain consequential authority.
Security should improve as confidential compute matures.
Where this is heading
AI infrastructure is moving toward verifiable private computation.
The likely enterprise pattern combines organisation-controlled data stores, identity-aware retrieval, model routing, least-privilege tools, confidential computing, attestation, retention controls, human authority and verifiable audit records.
TEMRIK's strategic position
The TEMRIK control plane between enterprise data, AI models, agents and authorised action.
The foundation model will change.Your company's operating memory should not have to.
Research basis
Built from current primary-source security architecture, not marketing folklore.
Provider features change. The links below are the primary references used for this page and should be checked again when designing a specific customer deployment.
Meta Engineering
Private Processing for AI workloads
Confidential VMs, encrypted memory, remote attestation and verifiable transparency.
Primary sourceConfidential Computing Consortium
Protecting agentic AI workloads
Hardware-based, attested Trusted Execution Environments for protecting data in use.
Primary sourceOWASP GenAI Security Project
LLM and agentic security risks
Prompt injection, sensitive data disclosure, excessive agency and insecure output handling.
Primary sourceOpenAI
Business data controls
Enterprise encryption, retention controls, Zero Data Retention eligibility and key-management options.
Primary sourceMicrosoft Foundry
Data, privacy and security
Customer data isolation, model-provider separation, geography controls and encryption options.
Primary sourceAmazon Bedrock
Data protection
Provider-isolated model deployment accounts and controls around customer prompts and completions.
Primary sourceGoogle Cloud
Vertex AI security controls
CMEK, VPC Service Controls, data residency and Access Transparency by feature.
Primary sourceAnthropic
Commercial data retention
Commercial/API retention terms and approved zero-data-retention arrangements.
Primary sourceFree field guide
27 Rules of Peace
Security is stronger when the operating rules are clear before AI enters the workflow.
Download TEMRIK's free construction AI field guide on decision rights, controlled escalation, evidence, playbooks and keeping people in authority when AI is involved.
Free guide · opens the TEMRIK Peace download page.
Control before scale
Before you give AI more of your business, decide what it is allowed to know.
TEMRIK can help map one workflow, its data, model access, permissions, actions and approval boundary before AI is scaled across the organisation.
No certification, confidential-inference availability or universal model compatibility is claimed by this page. Production controls depend on the selected provider, customer requirements and implementation scope.
The model can change.Your data, your rules, your knowledge and your authority stay with the business.