Turning Agents into enterprise workflows.
Currently, designing new features and products that help users investigate, summarize, question, refine, and act on cyber risk intelligence with clearer evidence and safer next steps.
Bitsight.
Designing AI-powered cybersecurity experiences for enterprise teams, connecting ratings transparency, risk prioritization, explainability, workflows, and platform execution.
Risk Management and Awareness.
Bitsight maps, spots, analyzes, classifies and rates risks found in company systems. Users goes from the most technical one looking to fix the issues, to senior managers handling reporting and compliance.
Designing key valuable strategic bets.
My focus right now is be part of building the Ratings Platform and AI design direction, aligning the user experience, product/ strategy, technical constraints, and customer value into something we can ship, validate, and scale.
AI + Risk Products.
Design Process.
My approach to cybersecurity work: from domain and customer understanding to product feature.
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01
Understand domain, mental models and opportunities.
Once an business opportunity/goal is framed, first layer is domain literacy and context setting. Collect quali/quanti inputs, then mapping current user journey, and understand the platforms and data behind it, identify pains and constrains, communicate outcomes to teams.
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02
Explore feasible opportunities aligned with strategy.
In cybersecurity, clarity is a product feature. I translate observations into opportunity areas: why a rating changed, what matters most, what should be fixed first, how risk is distributed, and what evidence supports the recommendation.
This helps teams move from “showing more data” to designing better decision support.
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03
Design AI behavior, not only AI screens.
For AI features, the interface is only the visible part. I work on how the system should reason, ask for context, expose evidence, handle uncertainty, invite feedback, and support users when the answer is incomplete.
The output is a product language for AI: useful, cautious, explainable, and connected to real enterprise workflows.
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04
Shape flows, systems, and product artifacts.
The work needs both platform logic and interface craft. I turn complex requirements into flows, page structures, interaction states, visual hierarchy, component patterns, and reusable artifacts that make the product easier to build and maintain.
For a cybersecurity platform, good UI is also governance: consistent states, naming, evidence, empty cases, permissions, and escalation paths.
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05
Partner through execution and launch constraints.
Enterprise AI and risk products need to survive implementation. I work closely with product managers, engineers, researchers, data teams, and leadership to sequence delivery, reduce ambiguity, and make trade-offs visible.
The prototype becomes an alignment tool: a way to discuss feasibility, data availability, system behavior, quality, and what can be shipped safely first.
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06
Measure clarity, confidence, and product value.
The work should not end when the feature ships. I define signals that help the team understand whether the experience improved understanding, prioritization, workflow completion, trust, and decision quality.
For AI experiences, measurement also includes answer quality, user feedback, task success, refinement behavior, escalation, and whether users can explain or act on the output.
Two consecutive years of successfully meeting area's goals.
My work connects enterprise cybersecurity workflows with research, strategy, system thinking, AI, and design craft.
Agentic UX
Workshops
Reports
Ratings & Risk
Infrastructure Management
Design Systems
User Research
Platform
Making ratings easier to report and improve.
Exploring ways to help users understand rating movement, risk vector behavior, finding impact, confidence, and the evidence behind important changes.
Helping security teams know what to fix first.
Designing new workflows that supports forensic investigation, prioritization, remediation, and progress across technical and executive audiences.
UX for reusable data patterns for product areas.
Establish cross-product UX around data standards (such as incubation periods, infrastructure, entity discovery), to blend context, strategy, and mental models to make product frameworks coherent and scalable.
Using visual maps to align context, issues, and opportunities.
Creating flow diagrams, system maps, decision trees, and design workshops to reduce ambiguity, expose constrains, and move teams from ambiguity to strong product vision.
Connecting design decisions to measurable product value.
Defining UX signals around comprehension, confidence, task success, time-to-insight, adoption, AI feedback, and the quality of decisions supported by the product.