PolicyPulse AI

Compliance policy is not easy to comprehend. On one hand, policy makers have troubles wording the policy to be too specific. On the other hand, developers have troubles understanding how the policy comes into play for their products.

The side effect of the above problem is that decision making for policy compliance can also be inconsistent. As each person interpret to his own context. For the same System, Party A says comply. Party B says non-compliant. This defeats the purpose of compliance policy which is to provide a consistent governance for all.

For the purpose of this hackathon, we would scope compliance down to IM8 security compliance.

How might we help product owners translate IM8 security requirements into actionable tasks for developers, ensuring compliance and faster SaaS/app deployment?

Team members and respective divisions

Problem Statement

After analyzing the key challenges product owners face in meeting IM8 security requirements during app service and SaaS deployments and through discussions with stakeholders, we identified that product owners often struggle to interpret IM8 within their system domain context, leading to miscommunication with developers and vendors. This misalignment results in delays, rework, and compliance risks. Existing compliance frameworks and tools provide guidelines but lack contextual adaptability, leaving product owners uncertain about what to ask and how to validate security requirements effectively. To address this, we explored the potential of LLM/GenAI to serve as a CSG-equivalent assistant, helping product owners translate security requirements in a structured, actionable way. By leveraging AI-driven insights, product owners can confidently assess IM8 applicability, ask the right questions, and communicate clear expectations to developers and vendors, ensuring faster, secure, and compliant deployments.

Problem Formulation Process

To define the problem accurately, we first identified the key stakeholders involved—product owners, developers, security teams, and vendors—and examined their pain points. Product owners often struggle with interpreting IM8 security requirements within their system domain, leading to unclear or incomplete guidance for developers and vendors. This misalignment results in miscommunication, compliance gaps, and deployment delays. Developers and vendors, on the other hand, lack a clear understanding of what is expected from them, making it difficult to implement security requirements correctly on the first attempt.

Next, we outlined critical questions to refine the problem scope. We asked: How do product owners currently translate IM8 requirements? Where do misinterpretations commonly occur? What are the bottlenecks in communicating security expectations? These questions helped us pinpoint gaps in existing processes and tools. We also evaluated available compliance frameworks and found that while they provide structured guidelines, they lack automation and contextual adaptability, leaving product owners uncertain about what to ask and how to validate security requirements effectively.

With these insights, we framed the core problem statement: How can an AI-powered assistant help product owners accurately interpret, validate, and communicate IM8 security requirements to developers and vendors, ensuring faster and compliant deployments? This problem statement encapsulates the need for an intelligent, context-aware solution that bridges the knowledge gap and streamlines compliance processes. Finally, we validated this problem statement with key stakeholders to ensure alignment with real-world challenges and deployment needs.

Solution

Tech Stack

Our current tech stack is built around a Streamlit app in Python, enabling users to submit queries about system compliance or a specific SaaS. The backend connects to a knowledge base of policy documents, retrieving relevant information for analysis. Once the relevant documents are identified, the system prompts an LLM over an API with the user’s query, ensuring responses are contextually accurate and policy-aligned. The client then returns a chat-based response, potentially accompanied by a compliance score report, giving users a clear and actionable assessment of their compliance status. This architecture ensures efficient document retrieval, intelligent query processing, and real-time compliance insights in a user-friendly interface.

**User Flow **

1. User Submits a Query

   • The user accesses the Streamlit app and enters a question regarding system compliance or a specific SaaS.
   • The query could be about IM8 security requirements, compliance gaps, or validation checks.

2. Knowledge Base Retrieval

   • The Python backend processes the query and searches the knowledge base of policy documents for relevant information.
   • The system identifies and extracts key sections from IM8 or other compliance guidelines to provide context.

3. LLM Query Processing

   • The retrieved documents and the user’s query are sent as a prompt to an LLM over an API for analysis.
   • The LLM synthesizes a response based on the retrieved compliance documents, ensuring domain-specific accuracy.

4. Response Generation

   • The LLM returns a chat-based response that answers the user's compliance question.
   • If applicable, the system also generates a compliance score report, highlighting areas of adherence or non-compliance.

5. User Receives Compliance Insights

   • The user sees the AI-generated response within the Streamlit interface, with interactive explanations and possible next steps for ensuring compliance.
   • The user can refine the query, ask follow-up questions, or request a deeper analysis.

This streamlined workflow ensures efficient compliance verification, clear policy interpretation, and actionable insights for product owners and teams.

Impact and Outcomes Analysis of the LLM-Powered Compliance Assistant

Impact

1. Accelerated Compliance Understanding

   • Product owners can quickly interpret IM8 security requirements within their system domain, reducing the time spent on manual research and document review.

2. Improved Communication and Alignment

   • The AI-generated responses help bridge the gap between product owners, developers, and vendors by providing clear, structured compliance guidance.
   • Developers receive precise requirements, minimizing back-and-forth clarifications and rework.

3. Faster and More Secure Deployments

   • By ensuring accurate compliance validation early in the process, organizations can reduce deployment delays and launch app services or SaaS solutions more efficiently.

4. Reduced Compliance Risks

   • The system proactively identifies potential security gaps, allowing teams to address compliance issues before deployment, lowering the risk of regulatory violations.

5. Scalability and Consistency

   • Unlike manual interpretation, the AI-driven approach ensures consistent and standardized compliance assessments across different teams and projects.

Expected Outcomes

1. Time Savings

   • Reduction in time spent interpreting IM8 documents, potentially cutting compliance review cycles by 50% or more.

2. Higher Compliance Accuracy

   • Improved adherence to security policies by providing context-aware, precise guidance tailored to each system’s requirements.

3. Increased Deployment Success Rate

   • Fewer last-minute compliance issues mean faster approvals and fewer security-related deployment failures.

4. Enhanced Decision-Making

   • Product owners gain data-driven insights into compliance gaps, enabling them to make more informed decisions about security measures.

5. Greater Adoption of Secure Development Practices

   • Embedding compliance guidance into the development workflow encourages proactive security measures, fostering a culture of security-first deployments.

By leveraging LLM-powered automation, this solution not only streamlines compliance workflows but also empowers product owners and developers to achieve secure and efficient SaaS deployments with confidence.

Future Steps

As we approach the end of the hackathon, the team thought that the project could evolve from a query-based assistant to a comprehensive compliance intelligence platform, enhancing security and efficiency in SaaS and app service deployments.

Here are some possible steps we could take: