RASED AI is the artificial intelligence layer of RASED, the cybersecurity company of Namra Tech. It applies machine learning and automation to threat detection, alert triage and incident containment, and it also secures and tests the AI systems clients deploy themselves.

Is RASED AI a product I can buy as software?

It is delivered as a service inside RASED engagements — managed SOC, incident response, assessment or AI security testing — rather than as a boxed licence. Scope, integrations and automation limits are agreed per client.

Will AI replace human analysts in your SOC?

No. Automation removes repetitive work — enrichment, correlation and first-line containment. Decisions that close a case, accuse a person or affect production are reviewed by a human analyst.

Do you send our data to public AI providers?

Data handling is agreed in writing before any engagement starts, including which processing stays inside your environment and what, if anything, may be sent to an external provider. Nothing is sent without that written agreement.

Can you test the chatbot or AI assistant we built?

Yes. We test AI features for prompt injection, jailbreaks, data leakage through model output, over-permissive tool access and insecure integrations, and deliver a prioritised fix list with retesting.

We have no SOC yet. Is AI the right first step?

Usually not. Detection needs reliable log sources, asset inventory and identity hygiene first. We start with an assessment, fix the foundations, then add automation where it changes outcomes.

How is RASED AI related to RASED Solutions?

RASED AI is one layer that runs across the RASED solution families rather than a separate company. It strengthens managed SOC, offensive security, incident response and governance work.

RASED AI — artificial intelligence applied to security operations, not marketing slides

RASED AI is the artificial intelligence layer of RASED, the cybersecurity company of Namra Tech. It works in two directions: it uses machine learning and automation to detect threats and cut analyst response time inside our security operations, and it secures and tests the AI systems our clients are now deploying themselves — models, agents, prompts and the data behind them. Every automated decision remains reviewable by a human analyst.

What RASED AI actually does

Six capability areas. Each one is delivered as a scoped engagement with defined inputs, measurable outputs and a named owner on both sides.

Behavioural anomaly detection — Signature rules catch what is already known. Behavioural models catch the account that suddenly behaves like somebody else: a login at an impossible hour, a service account touching a file share it never used, a mass export that no quarterly report explains. What it includes: Baselining of user, service-account and host behaviour، Impossible-travel and off-hours access modelling، Data-movement and mass-export detection، Continuous tuning against your real operating patterns. The outcome: Detections that survive contact with your environment instead of drowning the team in false positives.
Alert triage and enrichment — Most SOC time is spent on repetitive lookups. RASED AI enriches every alert before a human sees it: asset ownership, user role, prior history, threat intelligence context and a proposed severity — so the analyst starts from a briefing, not a raw log line. What it includes: Automatic correlation of related alerts into one case، Asset, identity and threat-intelligence enrichment، Suggested severity with the reasoning shown، Noise suppression for known-benign patterns. The outcome: Shorter time from alert to decision, and analysts spending their hours on judgement instead of copy-paste.
Automated response playbooks — Some actions should not wait for a human to wake up. Pre-agreed playbooks isolate a host, disable a session, block an indicator or force a password reset within seconds — always inside limits your team approved in writing, and always logged for review. What it includes: Playbook design workshops with your IT and business owners، Tiered automation: notify, contain, or contain-and-escalate، Full audit trail of every automated action، Rollback procedures and safe-mode boundaries. The outcome: Threats contained in seconds at 3am, with a defensible record of exactly what was done and why.
Security for your AI systems — Organisations are shipping chatbots, copilots and agents connected to real internal data. That creates a new attack surface: prompt injection, data leakage through model output, over-permissive tool access and unvetted third-party model providers. What it includes: Threat modelling for AI features before launch، Prompt-injection and jailbreak testing، Data-exposure review of retrieval and training sources، Permission review for agent tools and integrations. The outcome: An AI feature you can launch publicly without it becoming your next data-leak incident.
AI governance and policy — Staff are already pasting company data into public AI tools. Governance is not about banning that — it is about defining what may be shared, with which providers, under what approval, and how you would prove it to an auditor. What it includes: Acceptable-use policy for AI tools, written for real workflows، Data classification rules for what may leave the organisation، Vendor and model provider assessment، Staff awareness sessions on safe AI use. The outcome: A written, enforceable position on AI that legal, IT and the board can all sign.
Defence against AI-generated attacks — Generative tools removed the two cues staff were trained to spot: broken language and generic wording. Modern phishing is fluent, personalised and sometimes uses a cloned voice. Training has to change with it. What it includes: Simulations using realistic, well-written lures، Voice and video deepfake awareness for finance and executives، Verification procedures for payment and access requests، Reporting culture: measuring reports, not just clicks. The outcome: Staff who verify by process rather than by instinct — the only defence that scales against fluent attacks.

Where AI helps — and where it does not

We would rather set expectations correctly than sell a model as magic. These are the honest boundaries we work within.

AI is strong at scale and pattern: Reading millions of events, spotting a deviation from a baseline, and doing the repetitive enrichment work perfectly at 4am — this is where models genuinely outperform people.
AI is weak at business context: A model does not know that your finance team really does export the ledger every quarter, or that a merger explains a sudden spike in external access. Humans supply that context.
Automation needs written limits: An automated action that isolates a production server during business hours can cost more than the incident. Every playbook we deploy carries approved boundaries and an escalation path.
A model is never the last word: Confidence scores are not verdicts. Anything that closes a case, accuses a person or touches production is reviewed by a named analyst before it stands.

Why organisations choose RASED AI

Operators, not resellers: RASED AI is built by the same team that runs monitoring and incident response, so automation is designed around real analyst workflows.
Bilingual by default: Reports, playbooks and awareness material are produced in Arabic and English, which matters when your regulator reads one and your engineers read the other.
Human review on every decision: Automation acts within approved limits; judgement stays with named analysts, and every automated step is logged and reversible.
Both sides of AI security: We use AI to defend you, and we test the AI you deploy. Few providers in the region do both under one engagement.
Engineering backing: As part of Namra Tech, RASED can build and fix the integrations, not just report that they are missing.
Regional coverage: Operating from Mansoura, Egypt and serving clients across Egypt, Saudi Arabia and the Gulf.

Frequently asked questions about RASED AI

What is RASED AI? — RASED AI is the artificial intelligence layer of RASED, the cybersecurity company of Namra Tech. It applies machine learning and automation to threat detection, alert triage and incident containment, and it also secures and tests the AI systems clients deploy themselves.
Is RASED AI a product I can buy as software? — It is delivered as a service inside RASED engagements — managed SOC, incident response, assessment or AI security testing — rather than as a boxed licence. Scope, integrations and automation limits are agreed per client.
Will AI replace human analysts in your SOC? — No. Automation removes repetitive work — enrichment, correlation and first-line containment. Decisions that close a case, accuse a person or affect production are reviewed by a human analyst.
Do you send our data to public AI providers? — Data handling is agreed in writing before any engagement starts, including which processing stays inside your environment and what, if anything, may be sent to an external provider. Nothing is sent without that written agreement.
Can you test the chatbot or AI assistant we built? — Yes. We test AI features for prompt injection, jailbreaks, data leakage through model output, over-permissive tool access and insecure integrations, and deliver a prioritised fix list with retesting.
We have no SOC yet. Is AI the right first step? — Usually not. Detection needs reliable log sources, asset inventory and identity hygiene first. We start with an assessment, fix the foundations, then add automation where it changes outcomes.
How is RASED AI related to RASED Solutions? — RASED AI is one layer that runs across the RASED solution families rather than a separate company. It strengthens managed SOC, offensive security, incident response and governance work.