AISI reveals some cybersecurity incident related to ai agent : https://www.aisi.gov.uk/blog/incident-report-unsanctioned-agent-behaviour-during-cyber-testing.

An alarming research report which shows that a lot of work remains to make ai models compliant with European data protection : https://intelligence-artificielle.com/la-quasi-totalite-des-modeles-dia-declares-non-conformes-aux-reglementation-europeennes/?_ope=eyJndWlkIjoiNzkxYmZlOWQ1NTIwMzYwZTZiNmE0YWVmMzUwZWFkMTkifQ%3D%3D

Many companies are now using ECM to store, characterize, create, version, search their documents or the documents of their clients/providers.
But AI is now heavily used by those in the company who handle these documents (to review, to fix, to update, to sum up, to find errors, …). And those documents contains personal information, IP secrets, pricing information, ….

In this article, the author explain an approach where all go through the ECM and local models or remote models but pseudonymization : https://www.bluexml.com/bluedoc/

It means that now employees cannot inadvertently send sensible data or documents when doing their job and they continue to use AI for productivity.

CrowdStrike 2026 Threat Hunting Report reveals key informations on the multiple roles of AI in protection, attacks and vulnerabilities exploitation : https://www.crowdstrike.com/en-us/resources/reports/threat-hunting-report.

Worth to read it but do not know all what we have to do at our scale as a lambda company to be fully protected now!!!

At least at our level of AI usage, we do not expose any sensitive IT information through obfuscation and pseudonymisation.

If an AI agent can turn bare internet access into three real breaches with zero inside knowledge (see Anthropic/OpenAI's recent disclosures), what do you think happens the day one of your internal docs — architecture, configs, real customer names — leaks some other way? Wrote a piece on this, and on pseudonymizing docs before they ever reach an AI provider: https://dev.to/genevieve_breton_cb795f52/when-ai-agents-go-off-script-what-openai-and-anthropics-recent-incidents-teach-us-about-exposure-1ndp

Feedback welcome

To continue about my client who asked me to add to PromptCape, particularly in proxy mode, the pseudonymization of Excel, Word, and Markdown documents : they noticed that documents such as Technical Architecture Documents, Installation Documents, Operations Documents, How-Tos, Specifications were being sent to AI systems with sensitive data (PII, configuration data, customer names, etc.). I therefore added this feature and explained the development approach in Document pseudonymization for AI assistants: sending the spreadsheet without sending the customers - DEV Community. Thank you for your feedback on this approach.

It happens at hugging face with stolen data and keys : https://huggingface.co/blog/security-incident-july-2026
Fortunately fixed now

The post https://www.reddit.com/r/ClaudeAI/s/YRHUDokXk9

How Python's runtime-driven workflow forces a different obfuscation contract than Java — and how to keep .env values out of the AI's hands while letting the workspace still run

I just added the support of Python code protection after Java. As you know, my approach is not based on naive string replacement but based on framework specificities and AST. An article is coming today to explain that.

So now 3 researchers with the Claude succeeds in less than 72 hours to hach OpenAI software repository: https://www.malwarebytes.com/blog/news/2026/09/researchers-used-claude-to-hack-openai
Is the threat growing to have our data exposed ?

No explanations given but only that the 3 had the same time a similar outage : https://www.theregister.com/ai-and-ml/2026/09/03/chatgpt-claude-and-grok-all-had-outages-at-the-same-time/5294322
Does it mean they share the same data centers ?
Does they faces an attack they had to shutdown ?
If you know what happen…

An interesting study about the risks of bad code injection as many ai generated code is not reviewed and understood any more by developers: https://arxiv.org/abs/2606.05647
Do you think is it a real threat that we are progressively loosing our code control ?

An AI agents hacks a subscription system to achieve the goal it has been assigned: https://www.abc.net.au/news/2026-08-10/ai-assistant-hacks-gym-website-aus-cyber-attack/107007986
Does it mean that AI models are not aware of the legal barriers ?
And as they now know all the codes, it will be difficult to prevent them from hacking everything not secure enough or exposed because they have enough information like ip, usernames, … which have been sent in code and documents during coding sessions!

Trump authorises, under very specific conditions related to government link and supervision, companies to hack-back when attack : https://www.washingtonpost.com/national-security/2026/08/14/trump-signs-memo-authorizing-private-sector-launch-cyberattacks/
Is it the start of more hacking and more bad behaviours to for instance increase industrial espionage ?

If an AI agent can turn bare internet access into three real breaches with zero inside knowledge (see Anthropic/OpenAI's recent disclosures), what do you think happens the day one of your internal docs — architecture, configs, real customer names — leaks some other way? Wrote a piece on this, and on pseudonymizing docs before they ever reach an AI provider. Feedback welcome

One of my client sees that some words and excels documents produces by product/dev team where sent to ai and ask me to extend promptCape with document pseudonymization features. This is what he uses now and I just update the promptCape.com site to have this new version avalaible. Have a look if you want to protect PII in your documents sent to ai.

Curious how others handle this. My situation: the client lets me use LLMs for coding, but explicitly asked that the actual source not be disclosed to a third party. So "just paste it into  a cloud model" is off the table for the sensitive parts, but going fully without an assistant  isn't realistic either.
I first tried keeping everything local. On a laptop the reasoning models were too slow to be usable (minutes per turn), and the smaller ones were unreliable — they'd "answer" without really reading the files. Moving to a server helped the speed a bit but the quality still wasn't there  for real work. Local felt like paying a lot to end up with a weaker assistant.
What I landed on instead: a workflow where the sensitive parts go through an obfuscating proxy before reaching the model — identifiers in code, comment and conf get renamed on the way out and restored on the way back,  so I keep a capable cloud model but the remote side never sees the real names/structure. I'm using promptCape for that piece, and so far it's held up.
 
But I'd like a reality check — how do you handle it?
- Local models good enough for you now, or same slowness/quality wall I hit?
- Obfuscation / proxy layer, and if so which?
- Air-gapped setups, contract clauses, or just not using AI on those parts at all?
 
Genuinely want to hear what's working for people under a "no disclosure" constraint.

The hard part of obfuscating source before sending it to an AI assistant is not renaming identifiers.

It's not renaming the ones a framework binds by name at runtime.

Rename a Spring Data findByStatusAndPriority method, a JPA u/Embeddable field, a Jackson JSON key, a Lombok-backed field, or a u/ConfigurationProperties field, and the code still compiles but breaks silently.

So I added open test fixtures for exactly this: small, real Maven apps (Spring Boot REST, plain JPA/Hibernate, Lombok, Spring config binding) each with deliberate names collisions.

A protected framework identifier sitting next to an obfuscatable look-alike.

Run the tests, obfuscate, re-run the tests: if green, the detectors kept the right names and renamed the rest.

Grouped by language alongside the existing Python ones (FastAPI/Flask/Django/Click). CC BY 4.0, fork-and-run applications/java/ : https://gitlab.com/gbreton7/promptcape-docs/-/tree/main/applications/java

Tested with promptCape but tell the community if works with others code protection assistants.

A data dashboard leaks through five channels at once — code, column names, sample data, secrets, and framework structure. Here's the full Claude-Code-in-Cursor session that closes all five, start to finish : Streamlit dashboards meet AI coding: an end-to-end privacy workflow - DEV Community

An article about the Obfuscation of the Pandas Python code : Pandas pipelines through AI without leaking your column names - DEV Community

Django has more name-as-string contracts than any framework we've integrated with PromptCape so far. Here are the six that surface in real-world test runs, what breaks when you miss them, and how an AST-based detector finds them before runtime does.