AI security services in Dubai, UAE
Your staff are already using AI tools. We find which ones, decide with you which are allowed, and stop company data leaving in a prompt.
What AI security gives you
Four outcomes. The last one is the reason the first three are worth doing.
Your staff adopted AI before you chose a policy
In most organisations AI arrived through the browser. Somebody pasted a contract into a chatbot to summarise it, it saved an hour, and the habit spread across the department in a fortnight.
None of that shows up in a procurement record. It shows up as company data sitting on a service you have no contract with, under terms nobody read, with no way to get it back.
The response that works is not a ban. It is knowing what is in use, giving people a sanctioned tool that is good enough, and inspecting what leaves for the ones that are not.
AI security services we deliver
Most organisations start with discovery, because the argument about policy is much shorter once everyone can see the list.
AI application discovery
Which AI services are in use, by which departments and at what volume, established from network and identity telemetry instead of a staff survey.
Risk assessment per tool
What each service does with the data it receives, where it is processed, and what its terms allow, assessed per tool and not as a category.
Data exposure review
What has already been sent, so the position you inherit is understood before a new policy is written on top of it.
Embedded AI review
The assistants inside software you already own, which inherit user permissions and turn an over-shared folder into an answer.
Access control and policy enforcement
Which people may use which tools, granted by role through your identity provider so access is reviewable and reversible.
Data loss prevention for AI
Inspection of what leaves for a public AI service, with the categories you define blocked and the rest allowed through.
Sanctioned tooling
A small approved set with enterprise terms and tenant controls, so people have a route that does not require a workaround.
Permission remediation
Over-broad sharing corrected before an assistant is enabled, since it will surface whatever the user could already reach.
AI application testing
Prompt injection, insecure output handling and data exposure through retrieval, tested against the running application.
Tool and agent permissions
What an AI agent is allowed to call and with whose authority, which is where a small mistake becomes a large one.
Model supply chain review
Where models, plugins and extensions come from, and what they are permitted to reach once installed.
Vulnerability management
The infrastructure the AI stack runs on, kept in the same patch and remediation cycle as everything else.
User behaviour monitoring and coaching
Policy triggers turned into a short prompt at the moment it happens, which changes behaviour better than an annual course.
Responsible AI training
What staff may use, what must never be pasted, and why, delivered in the terms of their actual work.
Reporting and compliance auditing
Usage, blocks and exceptions reported monthly, in a form that supports the decision about what to allow next.
Policy review
The approved list revisited as tools change, because a policy written last quarter is already out of date.
Why organisations choose iConnect for AI security
We start with the list, not the policy
Discovery first. The argument about what to allow is much shorter once everyone is looking at the same list.
We do not recommend a ban
Blocking pushes the work to personal devices, where you have no visibility and no recourse. A sanctioned route works better.
We treat embedded AI as the harder half
Copilot and in-app assistants inherit user permissions. We fix the sharing before the feature is switched on.
We test what you build
Prompt injection and over-permissive agent access are tested against the running application, not reviewed on a diagram.
We calibrate before we enforce
Policy is tuned in monitor mode first, so the switch to blocking does not create a queue at the service desk.
We tie it to obligations you already have
The PDPL and your sector rules already cover this. We map controls to them instead of inventing a separate AI framework.
How an AI security rollout runs
We establish what is genuinely in use, from telemetry, because a survey returns the tools people are comfortable admitting to.
- Network and identity telemetry reviewed for AI service traffic
- Usage attributed to departments, which is what makes the conversation concrete
- Embedded AI features in existing software inventoried alongside the standalone tools
Each tool is assessed on what it does with the data it receives, not on the category it belongs to.
- Processing location and retention read from the terms that actually apply to your account
- Training use of submitted data established per service and per plan
- A recommended position on each tool: approve, restrict or block
The allowed set is agreed with the business, because a list drawn up by IT alone is a list people route around.
- A small sanctioned set with enterprise terms and tenant controls
- Access mapped to roles in your identity provider so it stays reviewable
- A written position staff can read in a minute, not a twelve-page standard
Inspection runs without blocking first, so the rules meet real traffic before anyone is stopped by them.
- Data categories defined against your business, not a vendor template
- False positives worked out of the rules before enforcement begins
- Permissions and sharing corrected ahead of enabling in-app assistants
Blocking is turned on in phases, paired with an explanation at the point it happens.
- High-risk categories enforced first, the rest staged behind them
- Users prompted at the moment of the block, with a sanctioned alternative named
- An exception route that works, so the policy is not bypassed to get work done
The approved list and the rules are revisited on a set cycle, since the tools change faster than the policy.
- New services appearing in discovery assessed as they appear
- Repeat policy triggers examined as a process problem, not a discipline one
- Reporting written for the decision about what to allow next
Where AI security meets UAE obligations
There is no single UAE AI statute yet. What already applies is data protection law, and putting personal data into an AI service is processing like any other.
UAE Personal Data Protection Law
Federal Decree-Law No. 45 of 2021 governs processing of personal data. Sending it to an AI service is processing, and the lawful basis, transfer and retention questions follow it.
DIFC and ADGM
Entities in the financial free zones sit under their own data protection regimes, with their own rules on automated processing and cross-border transfer.
Sector regulators
The Central Bank and the health authorities set expectations on outsourcing, confidentiality and where regulated data may be processed, and an AI service is a processor like any other.
ISO/IEC 42001
The management system standard for artificial intelligence, which gives you a certifiable structure if you need to evidence governance rather than assert it.
Sectors we secure AI for
What may be pasted into a public model differs entirely by sector, and so does the consequence of getting it wrong.
Government
Public records and citizen data, with residency questions that decide the answer.
Banking and Finance
Client confidentiality and regulator expectations on outsourced processing.
Healthcare
Patient data that cannot go to a general service, and clinicians who want the help.
Manufacturing
Designs and process knowledge that are the business, pasted in to save an hour.
Retail and E-commerce
Customer records and pricing, with AI now embedded across the marketing stack.
Education
Student data and very fast adoption, often ahead of any institutional position.
What our clients say
“Whenever an issue arises, iConnect is there immediately: quick, efficient and proactive in keeping everything running without disruptions. iConnect has become a crucial part of our operations.”
Head of IT Infrastructure and Network SecurityDragon OilAI security questions we get asked
Shadow AI is any AI tool in use across the business that IT did not approve and cannot see. It matters because the data going into it leaves your control the moment it is pasted. Blocking is rarely the answer, since the work still needs doing and people will find another route. Knowing what is in use is the first step to allowing it safely.
We can inspect what leaves for a public AI service and stop the categories you define, in the same way data loss prevention works for email and file sharing. What makes it succeed is calibration. Set it too tight and people move to a personal device, where you have no visibility at all.
No, and organisations that try usually end up less secure. The practical position is a small set of approved tools with enterprise terms, access granted by role, and monitoring on what goes into them. That gives people a sanctioned route that is good enough to use.
There is no single AI statute yet. What already applies is the Personal Data Protection Law, plus DIFC and ADGM regimes for entities in those free zones, and sector rules from the Central Bank and healthcare authorities. Putting personal data into an AI service is processing, and the existing obligations follow it. ISO/IEC 42001 gives you a certifiable management system if you want one.
This is the harder half of the problem. Copilot in Microsoft 365, assistants inside a CRM, summarisation in a support platform: each inherits whatever permissions the user has, so an over-permissive file share becomes an answer to a question. We review permissions and sharing before those features are switched on.
Those need testing, not policy. Prompt injection, insecure output handling, data exposure through retrieval and over-permissive tool access are the failures we look for, and they are tested against the running application. The OWASP Top 10 for large language model applications is a reasonable starting scope.
Which tools are in use and by which departments, what data categories were blocked and how often, which users repeatedly trigger policy, and how that trend moves month to month. It is written to support a decision about what to allow next, since that is the question a board keeps asking.
Discovery and a risk assessment run two to three weeks for most organisations and produce a list of tools in use, the data going to them, and a recommended position on each. Enforcement follows in phases, because turning on a full policy set in one weekend generates a queue at the service desk.
Talk to our team about your requirement
We’re happy to answer any questions you may have and help you determine which of our services best fit your needs.
Our Value Proposition
- Tailored Solutions for Your Organization
- Unbiased Expertise
- Proven IT Competence
- Outcome-Driven Approach
- Proactive Problem-Solving
- Transparent Processes
What happens next?
We’ll arrange a call at your convenience.
We do a discovery and consulting meeting
We’ll prepare a detailed proposal tailored to your requirements.
