Issue #7: When Prompts Go Rogue, Revenue Leaks

:Hey High Stakers,:

Good morning and welcome to the 9th issue of High Stakes!

🥋 When Prompts Go Rogue, Revenue Leaks 

Even when users don’t write prompts, they trigger them, and the risks show up in client-facing workflows. 

☕ 30-Second Brief

For sure, enterprise staff aren’t typing raw prompts into LLMs. But they are triggering AI assistants powered by them. When those prompts misfire, the result isn’t buried in logs. It shows up in live dashboards, client emails, or compliance reports.

💡 Prompt hygiene is now revenue hygiene.

In Feb 2025, a UK fintech pulled a GenAI chatbot after it leaked internal compliance language to a client, missed by guardrails, flagged by a relationship manager.

That same month, Anthropic showed how prompt injection still slips past safeguards, specially in multi-agent workflows (Anthropic Blog).

What’s changed? LLMs are now embedded in frontline workflows, they are drafting policy docs, shaping outbound proposals, and summarizing legal terms.

Today, the LLM isn’t behind the scenes, it is your interface.

This piece reframes that shift, for AI leads, GTM teams, and platform owners ready to make prompt security a growth advantage. 

For AI leads, GTM teams, and enterprise vendors, it’s time to treat prompt design as part of your commercial stack.

🧭 Where This Disconnect Shows Up

Across recent conversations - cyber teams, product owners, and AI platform leads - I kept hearing the same friction points. Here are the four that matter most:

⚠️ 1. CISOs still see prompt injection as a code-level threat

Even in mature institutions, most frontline employees aren’t engineering prompts. But they are triggering them. A Relationship Manager using an internal copilot to summarize onboarding tasks can still propagate risky output. CISOs often miss that these workflows aren't static. They're prompt-powered, and increasingly client-facing.

Most enterprise CISOs have been briefed on LLM safety. But their lens is still network-perimeter or data-loss-prevention.

Ask them if prompt injection could derail client adoption or tank renewal discussions. They’ll blink.

They’re securing the model. Not the moment of trust.

⚠️ 2. Red teams miss real-world prompt exploits

Security audits simulate SQL attacks, not context hijacks. Most don’t test if an agent can be manipulated via chained memory, embedded instructions, or tool-use escalation.

One GenAI PM told me:

“Our red team passed us. Then our intern got ChatGPT to crash the whole quoting flow with a single prompt.”

⚠️ 3. Guardrails are over-installed and underproven

Everyone’s buying LLM guardrails.
Few have run live stress tests across workflows.
Almost none track prompt drift over time.

Guardrails won’t stop a prompt that learns how to reframe its own input. They offer a helpful baseline, but not a substitute for deeper prompt architecture and resilience planning.

⚠️ 4. Prompt vulnerabilities only show up after GTM success

The risk isn’t from employees typing raw prompts, I have to repeat. It’s from seemingly safe workflows that route through brittle logic. As usage scales, prompt logic compounds silently. And by the time something leaks or misfires, it’s visible to the client, not just the dev team.

The moment your GenAI assistant starts generating value – talking to more users, handling more workflows, etc. – it also starts aggregating risk.

• More inputs

• More chained prompts

• More integration surfaces
  

That’s where the real attacks hide. And by then, it’s not just a risk.
It’s a revenue blocker.

📌 What GTM-Aligned Teams Should Do Right Now

If you’re integrating LLMs into production workflows, here’s the shift:

• Trace the full flow: Understand how a prompt moves through your system—from input to output

• Segment by task: Make sure AI memory resets cleanly between different jobs or client sessions

• Stress-test early: Run trial inputs to see where breakdowns might happen, before real users do

• Build visibility: Create simple dashboards that show whether the AI is handling prompts as intended, or going off-script

And crucially:
🎯 Frame prompt assurance as a value driver in your GTM materials.
Buyers don’t want security for compliance, they want it for confidence.

🧰 For Deeper Work: Helpful Artefacts (Customisable)

If you’re launching or scaling AI-driven products or services, I offer two artefacts that go deeper:

📎 1. Prompt Injection Red Team Kit

• 9 real-world exploit paths mapped to enterprise workflows

• Includes fuzz scripts, memory poisoning examples, and escalation scenarios
  

📎 2. Prompt Risk-to-Revenue Mapping Template

• Shows how prompt risks tie to churn, conversion, and contract language

• Designed to align GTM, product, and security leadership in a single workshop
  

To request these helpful artefacts, just reply to this email with “playbook“ or you can connect or follow me on LinkedIn to get daily actionable insights and I will send these to you as Google Drive links.

🚨 What They Won’t Tell You

Let’s be practical, no RM or support agent is building prompt chains from scratch. But they’re using tools built on prompts. And those tools can expose sensitive context in the wild if they aren’t tested like real interfaces.

This isn’t a technical issue, it’s a trust issue.
And trust isn't built with regex filters or marketing slides.
It's built with systems that behave reliably under stress.

In 2025, if your prompts aren't safe, your pipeline isn't safe.
Prompt hygiene is revenue hygiene.

And that’s the unlock: Prompt security isn’t a compliance obligation.
It’s a commercial enabler. The teams who master this will close faster, onboard safer, and scale smarter.

Your AI system’s real attack surface isn’t the API. It’s the conversation.

Are you designing for it? Or waiting to patch it?

Best,
Srini

P.S. If your AI workflows touch clients, you need prompt security baked into your revenue strategy.

Reply "playbook" to get the Prompt Injection Red Team Kit + Risk-to-Revenue Mapping Template, real-world tools to spot leaks before they cost you deals.