top of page
Cisco AI Assistant
Enhancing trust and transparency in AI-driven security solutions for network engineers.
Introduction
Organizations rely on security systems to protect against cyber threats, but misaligned policies and human error can create vulnerabilities, downtime, and reputational damage. This project focuses on enhancing trust through transparent, policy-compliant, and user-centric security solutions.
My Role
-> As a UX Designer and Design Strategist, I led the creation of the AI Insights Panel to build user trust through clear, transparent AI explanations.
-> I collaborated with stakeholders and engineers to design wireframes, prototypes, and user flows, emphasizing usability and transparency.
-> I also conducted usability testing, incorporating feedback to refine the design.
Project Duration
3 Months
Tools
Figma, Figjam, Google Docs
My Team
Apurva Patil
Neha Goswami
Nishant Mahendra Prasad
Cisco Stakeholders
Aanjan Ravi (Product Management)
Laura Tudorache (Product Design)
Molly Bloom (User Experience Research)
Akshay Parakh (Product Design)
Animesh Singh (Product Design)
Feixing Tuang (Product Design
Mentor
Professor Shuying Belivs
Problem Statement
Organizations face challenges in safeguarding their data and assets against cyber threats. A critical issue lies in ensuring security configurations align with their policies while minimizing human errors that can lead to vulnerabilities or downtime.
Our Approach
Organizations face challenges in safeguarding their data and assets against cyber threats. A critical issue lies in ensuring security configurations align with their policies while minimizing human errors that can lead to vulnerabilities or downtime.
Secondary Research Insights
Our research on using AI in network security has shown that working together, always making improvements, and sharing knowledge are very important.
Urgency of transparency & Mitigation AI risks for organizational trust
75%
Many large organizations employ AI behavior forensic experts to mitigate brand and reputational risks caused by AI system biases and misinterpretation of data.
Source: Gartner, Predicts 2021: Artificial Intelligence and Its Impact on People and Society” December 202
Fostering trust: Public calls for transparency in AI decision-making
58%
Americans cited increased transparency about algorithmic decision-making as a highly effective means to reduce bias in AI systems.
Source: Pew Research Center, “Public Attitudes Towards Computer Algorithms, “ June 2018
Gaps in Existing Solution
Opportunity: Prioritize transparency and explainability in its AI-driven features, providing users with clear explanations of AI-generated insights and recommendations. This can help build trust and confidence in the system's capabilities.
Opportunity: Focus on user-friendly interface design and customizable workflows. By prioritizing intuitive navigation, contextual help, and flexible configuration options, Cisco can enhance user experience and adoption rates.
Why should you care?
Risk Mitigation
By understanding AI decision-making and refining models iteratively, businesses can effectively mitigate risks, reducing the likelihood of security breaches and associated damages.
Efficiency Gains
Implementing these insights leads to more efficient security operations, as collaborative problem-solving and iterative model refinement streamline processes and reduce manual effort.
Innovation and Adaptability
Embracing knowledge sharing and continuous improvement fosters innovation, enabling organizations to swiftly adapt to evolving security challenges and maintain a proactive stance against emerging threats.
Our Mission
To promote trust in AI-driven solutions by enhancing transparency and explainability in AI decision-making processes for network engineers.
Our Mission
Trust
We strive to build confidence in AI-driven solutions by employing transparent metrics, gathering customer feedback, and utilizing decision visualization tools to enhance reliability and trustworthiness.
Transparency
We aim to provide clear insights into AI recommendations and decision-making processes, fostering understanding and trust among network engineers.
Collaboration
Our goal is to facilitate real-time collaboration and knowledge sharing amongst network engineers, promoting innovation and effective problem solving.
How does this strategy increase the trust and confidence of network engineers in AI?
Informed Decision-making:
AI Insights Panel provides detailed explanations, meter/confidence scores, and decision tree visualizations, enabling network engineers to understand AI-driven security decisions thoroughly.
Real-Time Guidance:
AI Guidance Panel offers real-time suggestions and best practices, supported by customer feedback ratings, facilitating efficient rule creation and modification for network engineers.
Collaborative Workspace:
Collaborative Incident Analysis features enable real-time collaboration and knowledge sharing among network engineers, enhancing teamwork and problem-solving.
Final Presentation
Explore More Projects
bottom of page