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27/06/2026 1:03 pm
TL;DR: AI assistants are now a common path for accidental secret exposure, and Entro Security says WebGuard scans prompts in the browser to block secrets and PII before they reach major LLMs. The governance gap is that current IAM and DLP assumptions do not see what users paste into AI tools in real time.
NHIMG editorial — what this means for NHI practitioners
By the numbers:
- When AWS credentials are exposed publicly, attackers attempt access within an average of 17 minutes and as quickly as 9 minutes in some cases.
Questions worth separating out
Q: How should security teams prevent secrets from being pasted into AI assistants?
A: Security teams should intercept prompts before they leave the browser, classify sensitive content in real time, and apply policy based on data type.
Q: Why do AI assistants create new risk for secrets management?
A: AI assistants create new risk because they turn ordinary user workflows into outbound data transfers that traditional controls do not always inspect.
Q: What breaks when prompt content is not inspected before send?
A: When prompt content is not inspected before send, organisations lose the chance to stop accidental disclosure at the browser boundary.
Practitioner guidance
- Define browser-prompt controls as a governed data path Treat prompts to AI assistants as a monitored outbound channel.
- Block high-risk secrets before model submission Use inline controls to stop production keys, tokens, and certificates at the browser boundary.
- Separate warn, block, and audit decisions by data type Do not force one response for all prompt content.
What's in the full announcement
Entro Security's full blog post covers the operational detail this post intentionally leaves for the source:
- How WebGuard classifies secrets and PII at prompt time across typed text, pasted code, and attached files.
- The exact block, prevent, and audit response modes and how each changes user workflow.
- Examples of what gets caught, including GitHub tokens, AWS access keys, and Stripe keys.
- How outbound events are redacted before forwarding into SIEM and audit workflows.
👉 Read Entro Security’s analysis of browser-side AI prompt secret scanning →
AI prompt scanning for secrets: can browser controls stop leaks?
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27/06/2026 2:13 pm
Prompt leakage is now an identity governance issue because the browser has become an unofficial secrets transport layer. Traditional IAM and DLP programmes assume sensitive data moves through known systems and policy-controlled channels. When employees paste keys, tokens, or customer records into AI assistants, that assumption fails and governance loses sight of the event before model interaction even begins. Practitioners should treat browser-visible prompt handling as part of the identity and secrets control boundary.
A few things that frame the scale:
- Two-thirds of enterprises have endured a successful cyberattack resulting from compromised non-human identities, with a quarter encountering multiple attacks, according to The 2024 ESG Report: Managing Non-Human Identities.
- The average organisation believes more than 1 in 5 of their non-human identities are insufficiently secured, which shows how broad the exposure surface already is across machine credentials and access paths.
A question worth separating out:
Q: Who is accountable when an employee leaks a secret into an AI prompt?
A: Accountability should sit with the organisation’s data, identity, and acceptable-use governance, not with the model itself. The practical question is whether the company defined prompt handling as a controlled data path, logged user decisions, and set response rules that match the sensitivity of the information involved.
👉 Read our full editorial: Browser-side AI prompt scanning closes secret exposure gaps
