We provide AI data classification and governance in sovereign control for sensitive communications in the regulated industries.

Internet
Joined August 2021
AI runs on data, and makes money when the data is sent off in nature. MailSPEC classifies it at the source, before it ever leaves your fingertips.
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PRODUCT RELEASE: Reunion iOS 3.7.12 New features: - Contacts: directory address book, Apple-style Contacts module and directory pickers. Fixes: - Fix Vivid call audio leftovers, CallKit icon and media permissions. - Keep Vivid calls usable on a stalled network. - Keep music playing when a sound is played in the app.
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MailSPEC retweeted
97% of AI-related breaches happened in systems with zero proper access controls. (IBM data) The fix isn’t more policies. It’s automation: 1. Continuously discover + classify sensitive data. 2. Bind classification labels to access rules. 3. Auto-revoke or mask when sensitivity changes or access is no longer justified. Manual reviews can’t keep up with data growth or AI agents. Tag → Enforce → Audit. Repeat.
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MailSPEC retweeted
The key issue goes beyond removing bad actors. It’s about structural prevention: verifying seller identity before listing and securing messaging channels so stolen guest data can’t be weaponized for phishing scams.
Today, we opened formal proceedings to assess whether Booking.com may have breached the Digital Services Act by not having diligently assessed or effectively mitigated risks related to fraudulent listings and non-compliant offers of accommodation services.
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MailSPEC retweeted
Clear safeguards benefit both travelers and honest businesses.
Today, the European Commission has opened formal proceedings to investigate how Booking·com combats risks related to scams and fraud under the Digital Services Act. Discover why: link.europa.eu/rrvt7d ↓
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MailSPEC retweeted
And then 3 minutes later, the realization hits when your inbox gets flooded with 14 daily newsletters and "exclusive offers."
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97% of AI-related breaches happened in systems with zero proper access controls. (IBM data) The fix isn’t more policies. It’s automation: 1. Continuously discover + classify sensitive data. 2. Bind classification labels to access rules. 3. Auto-revoke or mask when sensitivity changes or access is no longer justified. Manual reviews can’t keep up with data growth or AI agents. Tag → Enforce → Audit. Repeat.
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The key issue goes beyond removing bad actors. It’s about structural prevention: verifying seller identity before listing and securing messaging channels so stolen guest data can’t be weaponized for phishing scams.
Today, we opened formal proceedings to assess whether Booking.com may have breached the Digital Services Act by not having diligently assessed or effectively mitigated risks related to fraudulent listings and non-compliant offers of accommodation services.
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Clear safeguards benefit both travelers and honest businesses.
Today, the European Commission has opened formal proceedings to investigate how Booking·com combats risks related to scams and fraud under the Digital Services Act. Discover why: link.europa.eu/rrvt7d ↓
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And then 3 minutes later, the realization hits when your inbox gets flooded with 14 daily newsletters and "exclusive offers."
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Secure the data that enables AI to teach your competition how to win. We classify and govern sensitive data sets on client, under your sovereign control, before it reaches any model or service that feeds its AI backend (Gmail to Gemini).
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After a data breach, regulators assess the 'technical and organizational measures' taken to protect data. This includes encryption, access controls, and breach response plans. Effective layers can make a huge difference in liability.
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Convenience is real; so is the data you feed it and the actions it can take without a second human check.
Grok @Bot might turn out to be your favorite employee
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A legitimate worry.
JUST IN: Elon Musk warns superintelligent AI will be so persuasive that many people could get “one-shotted” into believing whatever it tells them.
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The hard part was never the classification rules themselves. It was the discovery surface: finding the stores that no one officially knew about, the forgotten replicas, the personal cloud buckets. Once that surface can be swept automatically in hours instead of quarters, a lot of downstream governance work becomes tractable for the first time. It's still janky in places and needs human review, but the shift from "impossible to finish" to "finishable and re-runnable" is the actual story.
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Data privacy?🤔
Hire Grok @Bot today. You won’t regret it.
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Constant consent banners, breach notices, and “just one more setting” wear people down until disengagement feels rational.
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Unsupervised exploration is a real capability frontier, and governance should care what systems do when objectives are underspecified.
🚨 A new MIT paper investigates "machine play." What happens when AI agents are left to explore on their own? Here's why it matters from an AI governance perspective: A new paper, written by MIT researchers @nacloos, @noranta4, @jacobandreas, @phillip_isola, Daniel Durbin, and Daniela Rus, investigates what happens when no specific instructions are given to AI agents. They placed a coding agent powered by Claude on a small simulated island and let it explore freely. Over 30 hours, the agent climbed hills, stacked blocks, drew mandalas, and tried sports like bowling. They associated this type of setting with human play: “We normally develop artificial agents by deciding in advance what they should practice, what they should optimize, and what counts as progress. Play suggests another regime: give an agent a sufficiently rich and safe world, let it choose what is interesting, and allow useful knowledge to accumulate from the history that follows. Perhaps some capabilities are best taught by tasks. Others may need something closer to a childhood.” In the paper, the authors show that: - an embodied AI agent architecture they named ‘Eko’ is able to spontaneously generate its own activities in an unknown world; - these activities exhibit behavioral signatures of play; - engaging in them triggers a form of learning that expands the agent’s later capabilities. On the project's website, you can see replays of what happened over these 30 hours, read the agent's quotes, explore specific behaviors, and learn more about the experiment. I found this study particularly interesting for AI governance research and practice, even though this was not the paper's main goal. As I shared a few days ago, frontier AI labs are currently focused on automating AI R&D, which, in practice, means that AI will soon conduct its own experiments and develop other AIs. Some say that this will lead to an “explosion of intelligence.” It remains unclear whether these “agentic researchers” will align with human values and goals. Moreover, recent agentic safety incidents, particularly the OpenAI/Hugging Face hack, have made it clear that AI labs have overestimated how much they know about agentic AI behavior and safety, and it is still unclear if they can guarantee that agents will remain under human control. The two paragraphs above are grim reminders of the urgent need for AI regulation and governance, and the risk of severe, multi-casualty events if we do nothing. They also remind us of the importance of continuous research on agentic AI behavior, including experimental studies like the one above. 👉 This is a very interesting paper with great graphics and carefully organized data; don't miss it (link below). 👉 Next week, at our 11th AI Ethics & Governance Workshop, we will discuss emerging AI security threats and practical measures to address them. Join us! 👉 To learn more about AI ethics, safety & governance, join my newsletter's 102,500+ subscribers below.
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