SAMPLE STRATEGIC CASE STUDY
How I would reposition a data security company for enterprise buyers as generative AI and AI agents change how sensitive data is accessed, shared and governed.
At a glance
Scenario: A data security vendor has strong discovery, classification and access-risk capabilities, but its positioning was built before enterprise AI became a major security concern.
Strategic challenge: Buyers now need to understand not only where sensitive data lives, but how AI applications and agents can reach, use and potentially expose it.
My approach: Expand the positioning from data visibility to AI-era data control, then carry that narrative through the website, product marketing, sales enablement and thought leadership.
The Market Shift
Enterprise AI adoption changes the data security buyer conversation. Generative AI tools, copilots and agents increasingly interact with the same enterprise data security teams are responsible for protecting.
The vendor’s existing message—focused primarily on discovering and classifying sensitive data—still matters. But it no longer answers the full buyer question.
Where is our sensitive data, who or what can access it, and what happens when AI becomes another way that data is used?
The Messaging Problem
The company could respond by adding phrases such as “AI-powered data security” or “secure AI” to its website. But those claims are quickly becoming category language.
I would instead use the AI shift to sharpen the company’s core value proposition.
The Positioning Strategy
I would organize the story around four buyer outcomes.
1. See the data that matters
Discover and classify sensitive and business-critical data across the enterprise environment.
2. Understand who and what can reach it
Add access and identity context, including AI applications and agents where relevant, so security teams can see where exposure exists.
3. Prioritize what creates real risk
Connect data sensitivity, access and usage context so teams can focus on material risk rather than another stream of alerts.
4. Act before exposure becomes an incident
Make remediation and governance part of the story so the platform is positioned as a path from visibility to action.
A Stronger Message
Instead of:
AI-powered data security for the modern enterprise.
I would test a narrative closer to:
Know where sensitive data lives, who and what can access it, and what puts it at risk as AI becomes part of everyday work.
How I Would Take It to Market
Website
Lead with the enterprise risk and control problem. Use product capabilities to prove how the company solves it.
Product marketing
Create a clear story connecting existing DSPM capabilities to emerging AI data risk without pretending the entire category has changed overnight.
Sales enablement
Give sales a narrative for conversations about sanctioned AI, shadow AI, agent access, sensitive data exposure and governance.
Thought leadership
Build content around buyer questions such as “What sensitive data can our AI tools access?”, “How should security teams manage shadow AI?” and “Where does DSPM fit in AI governance?”
Proof
Substantiate the story with concrete evidence: data coverage, access context, remediation workflows, integrations, deployment requirements and customer outcomes.
What This Demonstrates
This sample demonstrates how I would respond to a market shift without chasing buzzwords.
The goal would be to preserve what is already valuable about the product, identify what has changed in the buyer’s decision environment, and evolve the positioning so the company remains relevant and differentiated as enterprise AI adoption grows.
Related article
For the market context behind this strategy, read How Should a Data Security Company Position Itself for the AI Era? →
Is AI changing the way your buyers evaluate your data security platform?
Ruchira Agrawal helps B2B technology companies translate market shifts into clearer positioning, messaging and go-to-market strategy.