This article will give you tips on how to analyze responses from an API Developers survey about API security. Analyzing AI survey data can reveal key security blind spots and actionable insights fast.
Choosing the right tools for analysis
Your approach and tooling depend on the data structure you collected in your API security survey. For API developers, the mix of quantitative and qualitative data requires a slightly different workflow.
Quantitative data: If your survey includes numeric data (such as "how many developers rate API security as critical?"), you can quickly analyze these in Excel or Google Sheets. Tabulate, chart, and summarize trends for counts, percentages, or NPS scores within minutes.
Qualitative data: Open-ended responses, follow-ups, and conversational answer threads provide depth and nuance, but they're nearly impossible to analyze by reading each response manually. Modern AI-backed tools are essential here, allowing you to understand sentiment, themes, and outliers at scale.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Manual export and chat: You can export your response data (CSV or TXT) and paste it into ChatGPT, then prompt it to analyze themes or trends. This method is accessible and cheap, but it quickly gets messy, especially with larger datasets or when you want to dig into specific segments.
Workflow friction: Handling survey data in this way—exporting, copying, and pasting into generic AI—is difficult to scale and often leads to repeated work, unclear traceability, and a lack of collaboration tools. AI’s context window (how much text it can process at once) is often a bottleneck.
All-in-one tool like Specific
Purpose-built for survey analysis: Specific brings collection and AI-powered analysis together. It’s an AI survey tool that lets you create conversational surveys for API developers and analyze the responses instantly, all within one place.
Rich, context-driven data: By asking follow-up questions automatically, every response is deeper, richer, and less ambiguous—meaning when the AI summarizes the data, you’re getting real substance, not surface-level answers. Learn how the automatic AI follow-up questions feature improves your results.
No spreadsheets, no data wrangling: Analysis is instant: as soon as results flow in, GPT-based AI summarizes open-ended answers, finds key themes, and displays actionable insights. You can even chat directly with the AI about your survey results, just like in ChatGPT—but with special tools to manage which data is in scope for AI.
Team workflow ready: You don’t lose sight of individual feedback, either. Specific keeps conversations organized, tracks follow-ups, and makes it easy to filter and segment for deeper research. Take a look at this guide on designing API developer surveys for more on structuring better question types.
Useful prompts that you can use for API Developers survey about API Security
Prompts are how you get the most value from AI, whether using GPT directly or an integrated analysis platform. Here are some that work especially well with API security survey data:
Prompt for core ideas: Start here to get primary themes distilled from open-ended answers. This is the same analysis prompt that Specific uses by default. You can use it in ChatGPT as well:
Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.
Output requirements:
- Avoid unnecessary details
- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top
- no suggestions
- no indications
Example output:
1. **Core idea text:** explainer text
2. **Core idea text:** explainer text
3. **Core idea text:** explainer text
If you want stronger results, always give AI more context about your survey, your audience, and your goal. For example, before analysis, you might add:
This data comes from a 2024 survey of API developers working in SaaS companies, focused on their concerns, practices, and pain points regarding API security. Our goal is to understand where security best practices break down and what developers need to improve API protection.
Dive deeper into key ideas. After getting your core themes, drill down into them. For example:
Tell me more about incidents with authentication issues (core idea)
Prompt for specific topic: Use targeted queries to validate your hunches or confirm how often a particular issue was raised. Example:
Did anyone talk about OAuth vulnerabilities? Include quotes.
Prompt for pain points and challenges: Great for surfacing what’s broken or frustrating. Use:
Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned in relation to API security. Summarize each, with notes about patterns or frequency.
Prompt for personas: Useful for segmentation and product planning:
Based on the survey responses, identify and describe a list of distinct personas—similar to how "personas" are used in product management. For each persona, summarize their key characteristics, motivations, goals, and any relevant quotes or patterns observed in the conversations.
Prompt for sentiment analysis: Get a sense of the overall mood:
Assess the overall sentiment expressed in the survey responses (e.g., positive, negative, neutral). Highlight key phrases or feedback that contribute to each sentiment category.
Mix and match these prompts as needed—if you’re using Specific, these types of prompt-driven chats can be started with one click, and each conversation can be filtered or scoped as you want.
How Specific analyzes qualitative data for each question type
Open-ended questions: For each core question and every AI-powered follow-up, Specific generates a summary that pulls out the top themes and supporting quotes. You don’t have to read responses line by line.
Choices with follow-ups: When a respondent selects an option and gets a follow-up, those conversations are grouped. You get a summary for each choice, plus a synthesized report of what matters to people picking each answer.
NPS questions: Each segment—detractors, passives, promoters—gets a tailored summary, revealing motivations and suggestions tied to user loyalty or dissatisfaction.
You can do this with ChatGPT too, but it takes more manual work: first you have to group your responses by question or choice, then paste each subset into chat and run your prompts separately.
Dealing with AI context limits in large API developer surveys
AI tools have a limitation: the context window. If you have hundreds or thousands of conversations about API security, you can’t just paste it all into a single chat and expect a good output.
You can address this with two tactics (which Specific bakes in):
Filtering: Only analyze the conversations where users replied to selected questions or chose particular answers. For example, you might focus only on developers who reported multiple API security incidents (a good use case given that 57% of organizations had at least one API breach in two years, with 73% experiencing three or more [1]).
Cropping questions: Limit the questions you send to AI at a time. Select only the most critical ones—perhaps those related to emerging vulnerabilities, as highlighted by the surge in AI-related threats in APIs this year [2]. This process lets you break up big data sets, so analysis stays sharp and actionable.
Collaborative features for analyzing API developers survey responses
Collaboration is critical. When analyzing API security survey responses with a group—product managers, security leads, engineers—you need more than raw data. You need to see thought processes, track who analyzed what, and share insight along the way.
Analyze in AI chat threads: In Specific, you (or any teammate) can spark an analysis chat about a segment of data. Each chat is clearly labeled with the creator’s name, meaning you can see at a glance which colleague investigated which angle—maybe one chat for authentication pain points, another for monitoring strategies, and another for API security wish lists.
Multiple perspectives, no confusion: You can filter by survey responses (like "developers who report repeated breaches" or "those calling out AI-related threats" [2]), keep each thread focused, and see everyone’s contributions and avatars directly in the conversational interface.
Asynchronous insight sharing: Chats are saved and searchable, so anyone can come back and review the team’s findings. This streamlines group work, reduces duplicated effort, and lets different teams (engineering, product, security) work in parallel on the same data set, without ever losing context. If you haven’t tried collaborating this way yet, it’s a game-changer—especially as API security risks continue to multiply [3].
Create your API developers survey about API security now
Get answers that go deeper and insights you can act on—launch your survey, collect responses, and let AI handle the heavy lifting on analysis and reporting for API security.