SECURITY IS DESIGNED IN — NOT BOLTED ON.
DLP and masking before the model, one controlled path to every model, defenses against prompt injection, identities and permissions for AI agents, an audit trail and clear governance — built on the security stack you already run, so adoption doesn't create risk you can't see.
THIS DOMAIN INCLUDES //
DATA CLASSIFICATION & FLOW MAPPING
WHAT IT IS //
Classification labels data by sensitivity. Flow mapping shows where each class travels (which systems, models and providers), so every AI use case has a defined place to run.
WHAT WE DO //
During the audit we classify the data behind each use case and map its path end to end, then match each class to an allowed environment: on-premise, private cloud, air-gapped or public.
DLP & DATA MASKING
WHAT IT IS //
Data-loss prevention (DLP) keeps sensitive content from reaching a model or leaving the perimeter. Personal data, credentials and confidential material are detected, then blocked or masked — in prompts, uploaded files, retrieved documents and model answers.
WHAT WE DO //
We point the DLP and classification you already run at AI traffic and add AI-aware masking where it is missing: personal and critical fields are masked before a model sees them and restored only for authorized users. For public tools, the same rules stop regulated data before it reaches a prompt.
AI GATEWAY & USAGE CONTROL
WHAT IT IS //
An AI gateway is a single controlled path to every model your company uses. Unlike an ordinary web proxy, it sees the prompts and answers themselves, so it can enforce which models are allowed, who may use them, how much they may spend and what gets recorded.
WHAT WE DO //
We put one gateway in front of public and private models: a model allow-list and routing, per-team budgets and quotas, hooks for DLP and prompt screening, and full request and response logging in a store you own. It also keeps you vendor-neutral — switching a model becomes a configuration change.
PROMPT-INJECTION DEFENSE & TOOL SECURITY
WHAT IT IS //
Prompt injection is an attack in which text in a document, email or web page tricks an AI system into ignoring its instructions or leaking data. Agents that call tools raise the stakes: an AI that can act can be manipulated into acting — so both what goes into the model and what it may do need controls.
WHAT WE DO //
We threat-model each agent against recognized LLM risk lists (such as the OWASP Top 10 for LLM applications), screen inputs for injection and jailbreak attempts, isolate system prompts and check outputs. Tool use is controlled: an approved registry of tools and MCP servers, per-agent authorization, parameter checks, approval gates for high-impact actions and limits on how far one agent can delegate to another. We test with adversarial prompts before go-live.
IDENTITY & ACCESS FOR PEOPLE AND AGENTS
WHAT IT IS //
AI adds non-human actors to your organization: agents and integrations that act on their own. Access control for AI means every person, agent and integration gets only the permissions its job needs, and every action can be attributed — who asked, which sources were used, what the AI did.
WHAT WE DO //
We extend your identity system to agents: each agent gets its own identity and short-lived, narrowly scoped credentials, acts on behalf of a user only within that user's rights, and has no standing access to anything it does not need. Model hosts, GPU nodes and tool credentials sit under your privileged-access controls. Requests, sources, outputs and actions are logged in a store you own, so any action can be replayed afterwards.
AI INVENTORY & SHADOW-AI DISCOVERY
WHAT IT IS //
You cannot secure AI you do not know about. An AI inventory is a living register of the models, agents, tools, data connections and AI features inside SaaS products that your company uses — with an owner and a risk tier for each. Shadow AI is the use that sits outside it.
WHAT WE DO //
We find what is actually in use, approved or not, through network and endpoint signals, SaaS reviews and conversations with teams. We build the register with owners and risk tiers and route unapproved use to an approved alternative instead of simply blocking it. The register feeds your existing asset and risk processes and stays current as part of AI Operations.
SECURE AI DEVELOPMENT & SUPPLY CHAIN
WHAT IT IS //
AI applications and agents are built from more than your own code: packages, models, plugins, skills and tool servers from third parties. Each one is a possible way in. Secure AI development applies the discipline you already use for software (separate environments, reviews, gates) to all of it.
WHAT WE DO //
We separate development, test, staging and production, each with its own identities, secrets and data, and put a gate before every promotion: security tests plus model evaluation for quality, safety and prompt-injection resistance. Dependencies, plugins and tool servers are vetted and version-pinned, each system gets an AI bill of materials, and models and artifacts are scanned and signed before they are deployed.
AI GOVERNANCE & COMPLIANCE
WHAT IT IS //
AI governance is the set of rules for how your company uses AI: what it may be used for, with which data, who approves new uses and who is accountable. The rules are kept current as technology and regulation change.
WHAT WE DO //
We write the AI usage policy and decision rights with your legal, risk and IT teams, map the obligations that apply to you in each jurisdiction where you operate (UAE PDPL, GDPR where relevant, sector rules) and set up a review cadence and an incident playbook. Final legal sign-off stays with your counsel. AI Training for Business then trains your staff on the policy.
PROBLEM //
- Teams adopt AI tools nobody approved, and company data ends up in places nobody tracks.
- Agents get broad access "to make it work" — and nobody can say what they did.
- Compliance is an afterthought, so the first client questionnaire or audit exposes the gaps.
OUR APPROACH //
Security starts in the audit, not at go-live. We classify the data, decide where each class may run, and build filtering, access control and guardrails around the model. Most of that is the security stack you already run (identity, DLP, network and API controls, your SOC) pointed at AI. We add the few controls that are new for AI and stay vendor-neutral. You get evidence you can show a client or a regulator: logs, policies and test results. We came to AI from cybersecurity, so we start with what can go wrong — and design around it.
WHAT WE DO //
DESIGN
- Data classification and flow map
- Environment per data class
- Least-privilege model for people and agents
TEST
- Threat model per agent
- Adversarial and prompt-injection testing
- Security and model-evaluation gate before go-live
GOVERN
- AI usage policy and decision rights
- Obligations mapped per jurisdiction: UAE PDPL, GDPR, sector rules
- AI inventory, review cadence and incident playbook
PROVE
- Evidence pack for clients and auditors
- Logs and test results you own
- Regular review together with AI Operations
WHERE THE CONTROLS GO //
STAFF & PUBLIC AI TOOLS
- Approved tools only; unsanctioned use found and redirected
- DLP and masking set up to stop regulated data before it reaches a prompt
- Traffic goes through a gateway and is logged
YOUR APPS & PRIVATE MODELS
- Only approved models can be called; every request logged and budgeted
- App and API protection, segmented network
- Prompt screening and permissions-aware retrieval
AGENTS & TOOLS
- Own identity and short-lived, scoped credentials per agent
- Tools and MCP servers only from an approved registry, authorized on every call
- Controlled outbound traffic, human approval for high-impact actions, a replayable action trail
COMMON TO ALL THREE
- One usage policy and one AI inventory
- Logs and alerts flow into your SIEM and SOC
- Data classes decide what may go where
NEW FOR AI — WE ADD //
- AI gateway — one controlled path to every model: allow-list, budgets, full logging
- Prompt security — injection and jailbreak screening, system-prompt protection, output checks
- Tool gateway and tool security — an approved registry of tools and MCP servers, per-agent authorization, parameter checks, approval gates
- AI inventory and posture — every model, agent and tool with an owner and a risk tier; shadow-AI detection
YOUR EXISTING STACK — WE EXTEND IT //
- Identity and access — agent identities, short-lived scoped tokens, delegated access
- Privileged access — model hosts, GPU nodes and tool credentials under session control
- DLP and classification — your policies pointed at prompts, uploads, embeddings and answers
- API and web protection — WAF and API security covering AI apps and tool servers
- Network and egress — a segmented AI estate with explicit outbound rules
- SOC and pipeline — AI events in your SIEM; security gates, model scanning and signing in CI/CD
SECURE BUILD & SUPPLY CHAIN //
SEPARATE ENVIRONMENTS
- Development, test, staging and production kept apart
- Own identities, secrets and data in each
- No production data in development
GATES BEFORE PROMOTION
- Security tests and model evaluation must pass
- Quality, safety and prompt-injection checks
- The same gate for a new prompt, model or tool
DEPENDENCIES
- Packages, plugins, skills and tool servers vetted and pinned
- AI bill of materials for every system
- Models and artifacts scanned and signed
WHEN SOMETHING GOES WRONG
- Inventory of what runs and who owns it
- Execution trail from request to action
- Pause, revoke and isolate — built in before go-live, rehearsed under AI Operations
WHAT YOU GET //
- A data-to-environment map: which data may run where
- A control map: which layer covers which risk — built on the tools you already run
- DLP, access control, guardrails and logging built into the system
- Policies your legal team can sign and your staff can follow
- Evidence you can show a client, auditor or regulator
DATA// The most frequent blockers were staff functions, not end users; a seat in governance turns them into enablers. — Stanford Digital Economy Lab, 2026
PATH// security starts in the AUDIT, is designed in during DESIGN and is gated in every phase of the path — see all services and how it lands on the calendar.
- The UAE AI Act is not on the books — what regulation actually applies
- What a machine may decide — the execute / recommend / refuse boundary
- Private & On-Prem AI — where the sensitive core runs
- AI Operations & Value — monitoring and incident readiness after go-live
- AI Training for Business — training staff on the usage policy and safe habits