Enterprise AI security & secure enablement

Enable AI across engineering and operations without losing control of IP, data, identities, or actions.

Mankash helps enterprises design and, where scoped with confirmed delivery capability, implement customer-controlled security overlays for enterprise AI use, AI-assisted software engineering, and agentic workflows—covering approved model access, data protection, identity, MCP and tool gateways, sandboxed execution, prompt-injection defenses, auditability, and incident response.

Request a secure AI assessment Inspect the security overlay

Best fit: organizations enabling AI across multiple engineering or business teams where proprietary repositories, regulated data, internal tools, external model providers, or production actions are in scope.

Three connected workstreams

Secure the use of AI and the actions agents can take.

The architecture expands as capability moves from user interaction to code execution and cross-system action.

Enterprise AI access & data protection

Govern approved AI applications, model endpoints, browser tools, uploads, knowledge spaces, embeddings, connectors, provider terms, retention, residency, DLP, and shadow-AI visibility.

AI application and provider inventory
Data classification and routing policy
Gateway, DLP, redaction, retention, and audit design
Approved use patterns for prompts, uploads, indexing, and memory

Secure AI-assisted software engineering

Protect repositories, source code, developer endpoints, shells, packages, CI/CD, credentials, issue and pull-request content, web access, and coding-agent instructions.

Sandboxed and task-scoped workspaces
Short-lived credentials and egress controls
Package, branch, review, and CI/CD safeguards
Separate controls for assistants, local agents, cloud agents, and review bots

Secure agentic workflows

Constrain agents operating across email, documents, RAG, MCP, internal APIs, databases, memory, inter-agent communication, and production actions.

Tool and connector allowlists
Action-level authorization and approval tiers
Prompt-injection containment and untrusted-content boundaries
Traceability, exception handling, rollback, and kill paths

Exposure paths

Sensitive material can leave long before an agent takes an action.

A useful threat model follows data through prompts, retrieval, providers, logs, memory, tools, and execution—not only the final response.

User & endpointClipboard, browser, IDE, device, extensions
Prompt & contextUploads, source code, policies, customer data
Retrieval & memoryIndexes, embeddings, caches, conversation state
Model & providerAPIs, routing, retention, training terms, subprocessors
Tools & MCPConnectors, credentials, arguments, returned content
Execution & actionShells, CI/CD, databases, email, production systems
Evidence & responseTraces, logs, alerts, rollback, investigation

Operating mode	Expanded reach	Primary control emphasis
Enterprise AI use	Prompts, files, knowledge spaces, model APIs, histories, embeddings, and connectors	Approved access, provider terms, classification, routing, DLP, retention, residency, and audit
Coding assistants & agents	Repositories, shells, developer endpoints, packages, CI/CD, secrets, issue/PR systems, and web access	Sandboxing, task-scoped credentials, egress, package controls, branch protection, review, and secure-development gates
Workflow agents	Email, documents, RAG, MCP, APIs, databases, memory, other agents, and production actions	Agent identity, tool authorization, injection containment, approval tiers, transaction limits, monitoring, and rollback

Reference architecture

A six-layer security overlay for enterprise AI.

This is an inspectable reference method. Controls are selected and tested against the customer’s architecture; the diagram is not a claim of certification or completed customer deployment.

Inventory, identity & access

Discover approved and unapproved AI use; assign human, workload, and agent identities; enforce SSO, MFA, RBAC/ABAC, separation of duties, and task-scoped authorization.

AI asset and usage inventory
Human, service, and agent identity
Least privilege and just-in-time access
Owner, purpose, and expiry for every integration

IP & data protection

Classify prompts and context, minimize exposed data, redact or tokenize where appropriate, control retention and residency, and prevent secrets or protected content from entering unapproved paths.

Source-code and document boundaries
DLP and content inspection
Secrets detection and credential isolation
Logs, traces, memory, embeddings, and deletion policy

Model & AI gateway

Route approved traffic through policy enforcement that can select allowed providers and models, apply data rules, meter use, and create audit evidence without logging sensitive content indiscriminately.

Provider and model allowlists
Policy-based routing and private connectivity
Rate, cost, and context controls
Retention-aware telemetry and safe error handling

Tool, connector & MCP gateway

Treat tool descriptions, MCP servers, connectors, and retrieved content as untrusted inputs. Authenticate servers, constrain capabilities, validate parameters, and authorize the action—not merely the chat session.

MCP server registry and provenance
Typed tool schemas and argument validation
Per-tool credentials and scopes
Read/write separation, approvals, and output sanitization

Sandboxed coding-agent & execution runtime

Run code and commands inside bounded workspaces with restricted filesystems, network egress, process capabilities, packages, credentials, time, and compute.

Ephemeral or isolated workspaces
Repository and branch boundaries
Egress and package-source policy
Command, filesystem, and production-action gates

Assurance, detection & response

Test normal and adversarial behavior, define release gates, monitor control failures and drift, preserve decision evidence, and prepare containment, revocation, rollback, and incident response.

Evaluation and regression suites
Authorized adversarial testing
SIEM/SOC integration and actionable alerts
Kill paths, credential revocation, rollback, and lessons learned

Deployment patterns

Place enforcement where the customer can own the boundary.

Final design depends on provider capabilities, data classification, latency, operations, and support—not a blanket promise of privacy or isolation.

Enterprise-managed SaaS

Approved enterprise AI services with contractual data controls, tenant administration, identity integration, provider configuration, and monitored use.

Customer-controlled gateway

Central model and tool access through customer policy, identity, routing, DLP, cost, and audit controls while retaining approved external providers.

Private VPC or on premises

Models, gateways, sandboxes, or workflow components inside customer-controlled infrastructure when risk, residency, or integration needs justify the operational cost.

Engagement path

Assess exposure, establish the landing zone, then assure change.

Architecture and implementation are separated so the control plan can be reviewed before a larger commitment.

Entry assessment

Secure AI Exposure & Architecture Assessment

Inventory AI use and critical paths, model sensitive data and actions, review providers and current controls, prioritize gaps, and define a target security overlay.

Exposure and asset map
Threat scenarios and trust boundaries
Control-gap and priority register
Target architecture and implementation roadmap

Paid scope follows the initial no-charge consultation.

Implementation

Secure AI Landing Zone

Implement approved identity, data, gateway, MCP/tool, sandbox, policy, telemetry, and release controls with the customer’s platform and security teams.

Golden paths for approved AI use
Coding-agent runtime pattern
Workflow-agent action pattern
Control tests and operating runbooks

Subject to confirmed delivery roles and customer access.

Ongoing assurance

AI Security Assurance

Review architecture change, control evidence, agent releases, regression and adversarial results, incidents, exceptions, providers, and the evolving risk register.

Release and exception review
Evaluation and control evidence
Threat and provider updates
Incident learning and roadmap refresh

Not a managed SOC, certification, or audit opinion.

Claim boundaries

Security architecture must be testable and precisely scoped.

Named architecture lead: Baljit Singh, Founder & AI Architect, leads the principal architecture mandate. The customer retains its accountable CISO, AppSec, privacy, legal, and risk authorities.

What this service is: architecture, threat modeling, control design, implementation where scoped, and assurance planning for customer-controlled environments.

What it is not: a guarantee against data loss or prompt injection, a packaged certification, legal advice, an audit opinion, or an unqualified claim of penetration-testing capability.

Framework use: controls may be mapped to customer-selected NIST, OWASP, MITRE, ISO, cloud, and internal standards. Mapping demonstrates coverage intent; it does not establish compliance or certification.

Specialist execution: penetration tests, red-team exercises, and regulated assessments occur only under written authorization, an explicit testing boundary, and appropriately qualified delivery resources.

FAQ

Secure enablement without absolute promises.

Detailed architecture, vulnerabilities, incidents, repository names, credentials, and logs belong in an approved secure discovery process—not the public consultation form.

Is this only agent security?

No. Agent security is one workstream. The scope also covers enterprise AI access, provider and data controls, and AI-assisted software engineering before autonomous workflows are introduced.

Can you prevent our source code from reaching a public model?

We can design controls around approved providers, routing, DLP, repository access, sandboxes, egress, retention, and audit. The achievable boundary depends on the customer environment and provider contracts; no website claim can replace implementation and testing.

How do you address prompt injection?

Prompt injection is treated as a system-boundary problem. Controls include untrusted-content labeling, data/tool separation, least privilege, action authorization, schema validation, sandboxing, approvals, monitoring, and adversarial tests. No single prompt or filter eliminates the risk.

Do you secure MCP servers?

We can assess and design MCP inventory, provenance, authentication, capability scopes, tool schemas, credential isolation, network policy, action approvals, logging, testing, and revocation. Implementation depends on the selected MCP clients, servers, and customer controls.

Can the solution run in our VPC or on premises?

Yes, those are possible deployment patterns subject to product, model, security, support, and access constraints. The assessment determines which controls remain customer-owned and which can be implemented by Mankash or approved partners.

Do you provide penetration testing or a certification?

This offer is architecture and secure enablement. Specialized penetration or red-team execution is separately authorized and scoped with qualified resources. Framework mapping is not certification, an audit opinion, or a guarantee of security.

Will you replace our CISO, AppSec, or privacy teams?

No. We translate AI-specific risks into architecture and implementation decisions with the customer’s accountable security, privacy, legal, engineering, and risk owners.

Secure AI assessment

Map where AI can reach your IP, data, tools, and production actions.

Describe the scope category and protected assets at a high level. Do not submit sensitive architecture details or security findings through the website.

Request a secure AI assessment