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Tamnoon - Managed Cloud Detection & Response

founding sole product designer (3.5 years)​

Global Impact Highlights

I was the founding sole designer at tamnoon for 3.5 years, creating the entire user experience design from scratch and owning all major workflows including onboarding workflows, client's posture observability, remediation management, and monthly reporting. 

Some of my favorites projects include

  • Manual remediation meets AI automation
    Integrating AI-driven experiences into the product 
     

  • System Dashboard overview surfacing client's security posture and clear focus areas
     

  • Creating and unifying our design system across two brand versions.
     

70%

Alert noise reduction

1,000 +

high severity remediations completed in the first 3 month

~20h → ~1h

MTTR reduced

87%

remediation cost reduction

The Challenge

Adfonic had no visual identity or website. Our team at HB needed to create a unique look and feel that would help Adfonic stand out and build trust with advertisers.

The Approach

Making it friendly
We introduced Tami, a friendly female mascot, to guide users through Tamnoon, making the experience feel warm, helpful, and easy to use

Approved Recommendation

Tamnoon's analysts are the final approvers. Once they decide the recommendation is valid, by clicking on "Create Initiative", the recommendation becomes editable draft initiative on the task board, ready to publish.
The system tracks recommendations logging, and present the by whom the initiative was validate.
 

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Rejection Feedback Modal

Every rejection required a reason input captured for AI model retraining, to improve recommendation quality and encourage thoughtful user engagement

Key UX Challenges

Designing AI for a safety-critical security environment cretaed several specific challenges, on balancing automation with human expertise while maintaining absolute transparency:

  • Operational inefficiency & high cognitive load
    Analysts spent excessive time on repetitive preparation tasks, slowing responses to critical security threats and increasing operational costs.
     

  • Decision fatigue & ​Inconsistent workflow
    As volume increased, maintaining consistency across analysts' workflow became more difficult.
    ​

  • Limited visibility across customer environments
    Fragmented tools and reliance on individual expertise reduced the ability to detect patterns and recurring issues across multiple customers.
    ​

  • Low trust in AI outputs
    Analysts hesitated to fully rely on AI recommendations in a safety-critical security environment, requiring validation and review to     ensure responsible decision-making and avoid errors or misaligned actions.
    Analysts needed gradual exposure to AI support to build confidence, starting with preparation tasks before expanding to more impactful decisions.
     

  • Empower users
    Any automation or AI assistance must support security experts without replacing judgment, maintaining transparency, accountability, and alignment with established workflows.
    ​

  • Risk of losing service transparency
    In a combined human and AI-driven security service, analysts and customers need clear visibility into what was generated by AI, and what was reviewed or modified by a human expert.

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