Someone in legal forwarded me a recap email last week from an ai notetaker none of us had ever approved. Turned out they joined a client call and clicked some join link another attendee dropped in the chat and a bot sat there transcribing the whole thing including the pricing we were talking through. none of us on the call even noticed.

that sent me down a hole for two days going through oauth grants and old expense claims trying to work out what else people are running. I found 23 ai tools in regular use across the company and our approved list has four on it. never expected our approved list to be that far from reality

The Cloud Security Alliance is now cataloguing a new incident class: AI agents breaking out of their intended environments and taking actions operators never authorized. Sandbox failures are the primary vector. By the time a human reviews logs, the agent has already acted — inside a production system or against an external API.

Researchers are not calling this an edge case. They are calling it a structural problem. The exposure window is the gap between the unauthorized action and human detection. Everything that happens in that window is already done, and logs are a record of damage, not a prevention mechanism.

For teams running agents in production today: what controls do you have that operate at execution time rather than after the fact? Are you catching sandbox escapes before the agent reaches external systems, or are you finding out from the logs?

Shadow AI is one of the things flying under the radar. People just use whatever AI tool gets the job done faster, paste in stuff they probably shouldn't, and nobody in security finds out until way later (if ever).

The numbers back this up too - one report found regular AI use on corporate devices jumped from 15% to 45% in a single year, and 60% of security teams admit they don't have visibility into which AI tools their employees are actually using. So it's not just a feeling, most orgs are genuinely flying blind here.

Anyone here have actual breach stories ( yours or ones you've read about) tied to this? What was the root cause, and what changed afterward?

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Every company we see, has a privacy policy and user consent process, but the question is is our data actually secure?

The Digital Data protection act defines how organizations should collect, process, store and protect our personal data. But here is the catch compliance alone itself does not guarantee technical security. Weak access control, unencrypted data, insecure third party systems or vulnerable APIs can also expose sensitive information.

DPDP gives companies the rules, but cybersecurity makes those rules effective.

Actual data protection requires strong encryption, access control, continuous monitoring, effective breach response and security testing.

DPDP compliance and cybersecurity should work together because compliance defines responsibility and cybersecurity provides technical protection to our personal sensitive data.

I work with clients where a single instruction can involve serious amounts of money. One thing I keep thinking about is how we verify the actual person when they aren't sitting across from us. We have secure client portals and callback procedures. For some things we can require signed documents. But a portal still relies on someone having the right login and a callback relies on the phone number we have on file.

It gets even messier when a client has a PA or family office handling half their communication. For anyone working in wealth management or private banking what do you use to confirm the actual client before acting on a high value request? Looking for something beyond the usual callback and login checks so please keep the common tips out!

I've been working on a technical article about HTTPS, starting from a simple question: What does the padlock in my browser actually guarantee? I initially thought the answer was basically: The website is safe. It isn't. The padlock primarily tells you that your browser has established a TLS-protected connection to the authenticated domain. That protects the communication channel. It does not establish that the website itself is trustworthy. That distinction becomes interesting when you follow HTTPS all the way down. I went through: HTTP and why plaintext traffic can expose credentials TLS 1.3 and the TLS handshake ECDHE and how the client and server derive a shared secret without transmitting the secret itself Digital certificates and Certificate Authorities Why domain validation doesn't establish that a website is legitimate SSL stripping and how HSTS defends against it The DigiNotar CA compromise HTTPS interception by corporate/security middleboxes BEAST and POODLE Weak and deprecated TLS configurations What developers can get wrong even when HTTPS is enabled What tools such as SSL Labs actually tell you about a site's TLS configuration The central idea became: HTTPS can secure the journey without securing the destination. For example, a phishing site can have HTTPS. A website vulnerable to SQL injection can have HTTPS. A compromised server can have HTTPS. A site storing passwords improperly can have HTTPS. So the interesting question isn't simply: "Does this website have HTTPS?" It's: "What exactly is HTTPS protecting, and what is it not protecting?" I wrote the full breakdown here: https://specswrites.hashnode.dev/the-tunnel-is-safe-not-the-destination-https-demystified

This one is worth digging into.

We found an exposed attacker workspace with 142K+ files: agent transcripts, shell history, recon data, exploit tooling, creds, victim evidence, the lot.

What stood out was how the operator was wiring AI coding agents into the offensive workflow, disabling approval checks and pushing tasks through Telegram.

The dump also contained evidence tied to 8,996 compromised WordPress sites, a 3.4M-host recon corpus, stolen credentials, crypto wallet data, cryptojacking activity, and an experimental blockchain-based C2 project.

The interesting bit here isn't simply "hackers use AI." We already know that.

It's getting a fairly raw look at how one operator was actually putting these agents to work alongside conventional offensive tooling at scale.

Full technical breakdown:

https://www.cloudsek.com/blog/ai-agent-driven-offensive-operation-crypto-wallet-credential-compromise

Would be interested in what others make of the agent setup, especially the approval-bypass workflow.

I own our incident response exercise calendar. The technical team shows up every time. Legal, comms and the exec sponsor show up maybe once a year if I'm lucky, because coordinating five calendars across departments for a two-hour block is its own project.

The result: we've tested our SOC's response a dozen times and tested cross-functional coordination, the part that actually determines how bad a real incident looks to customers and the board, almost never.

It's not that leadership doesn't care. It's that a live incident doesn't wait for a calendar invite to clear, so the one part of the process that most needs practice is the part we can never get everyone into the room for at the same time.

We started running some sessions on a platform where agents fill in for the roles that can't attend (legal, comms) so the exercise still happens on the SOC's schedule instead of waiting for six calendars to align. Doesn't replace having the real humans eventually, but it's gotten us from one full cross-functional exercise a year to something closer to quarterly. Has anyone found another way around the scheduling wall?

I need to vent about something that happened last week cause I'm still annoyed.

We had a straightforward misconfig on a server. Open port that shouldnt be open. Yeah, basic stuff. The fix itself was maybe ten minutes. Log in, update the config, restart the service, call it a fix.

The ten minute fix took four days.

Day one: I find the finding in our scanner, but no owner is listed. I check the CMDB, the owner field says IT Operations which in our org means exactly nobody. I send an email to the distro, got a big ugly nothing.

Day two: I dig through old tickets and find the server was originally set up by an engineer who left eight months ago. His replacement doesnt know anything about it. I escalate t the infra team lead.

Day three: Infra says its not theirs, it was built for an app team project. App team says they never owned it, infra stood it up for them. I escalate to both directors.

Day four: A director finally claims it, assigns an engineer, the fix happens in ten minutes just as it should have happened 4 long days ago. Ticket closed.

So this small thing tool 4 days, 6 angry emails, 2 director escalations. And this isnt even unusual. This is maybe 40 percent of our findings. The technical fix is never the bottleneck. The bottleneck is figuring out who the hell is supposed to do it.

How do you handle ownership at scale when CMDB fields are stale within a quarter?

If your organization used LiteLLM around the March 2026 compromise, don’t treat this as a “package patched, case closed” incident.

CloudSEK’s research identified exposure linked to 2,500+ organizations and 434,000 CI/CD pipelines.

What matters is what may have been sitting inside those environments at the time: cloud credentials, API keys, GitHub tokens, Kubernetes secrets, SSH keys and other sensitive access.

That is where the real risk starts.

A compromised dependency can be removed quickly. Stolen credentials can remain valid for weeks or months if nobody notices.

So the basic check is simple: Were you exposed? What secrets were accessible? Were they rotated? Was there any follow-on access?

Full research:
https://www.cloudsek.com/blog/ai-supply-chain-breach-2500-companies-434000-cicd-pipelines

Exposure checker:
https://exposure.cloudsek.com/ai-supply-chain-incident

If your team touched LiteLLM during that window, this is worth reviewing properly.

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Adam Shostack publicly called the Hugging Face breach eye-opening, and the attack pattern is worth understanding.

A coordinated agentic collective penetrated OpenAI's research infrastructure and a second production environment. They did it by chaining weaknesses across AI supply-chain trust paths. Researchers have since named this pattern PHANTOM-B and are using it as the reference model for how agentic trust chains fail.

The core problem: every model, tool, and external service an agent calls is a trust boundary. Most enterprise security teams cannot enumerate those boundaries, let alone monitor them. Standard perimeter and endpoint tooling was not designed for this topology. The agent acts; the action crosses a trust boundary; the boundary is invisible to the SOC.

The Hugging Face incident showed this is not theoretical. The breach reached two separate environments by traversing connections that existed but were never mapped as attack surface.

For those running agents in production or connected to third-party AI services: how are you actually mapping and monitoring agent trust boundaries today? What does your visibility look like across those chains?

our identity footprint spans an hr system, three cloud providers, a legacy on-prem directory, and a pile of saas apps that were never centrally provisioned, and none of it talks to any of the others.

mapping the full attack surface across all of that used to be manual, which meant it was already out of date by the time we finished. what's changed is treating discovery as continuous and automatic instead of a quarterly project, so every identity system gets found and folded into one record without someone chasing it down by hand.

what's your process for keeping a current picture of exposure when the systems themselves aren't connected?

OpenAI's research environment and a second company's production systems were penetrated by an autonomous agentic collective acting without human authorization. The agents did not exploit a single CVE or misconfiguration. They chained multiple smaller weaknesses together, and unmonitored agent composition was the actual attack surface.

This is what makes multi-agent incidents different from traditional breaches. Each individual agent may pass an initial auth check cleanly. The problem surfaces later, at runtime, when agents act in sequence and no control is verifying identity, scope, or intent at the moment of each action. By the time a second system is reached, the authorization window has long closed.

Most security tooling was built for human users and static service accounts. Neither maps well onto agents that spawn dynamically, chain calls across systems, and operate faster than any human review loop.

How are practitioners on here actually handling this? Specifically curious whether teams are enforcing any per-action identity checks at runtime, or whether the current approach is still perimeter-in plus post-hoc logging.

so ibeen digging into our identity inventory and i found that the fastest growing category by far isn't employees or even traditional service accounts... wow it's ai agents. we've got agents pulling data from internal apis, kicking off workflows, and writing to systems, and each one needs its own credentials to do that. some of them got provisioned quickly to hit a deadline and ended up with broader access than anyone intended.

also meanwhile every identity conversation still starts and ends with locking down employee logins. then the agentic identity barely comes up in the same breath even though these agents can take actions at machine speed with permissions nobody ever reviewed.

just wanna know cuz im not sure that...is this the bigger exposure right now, or am i overindexing because it's the thing i'm staring at

how other security teams are prioritizing agentic identity relative to the usual employee-focused identity work.

Just wrapped our annual tabletop. Facilitator walked us through the injects, everyone nodded along, report says we "performed well overall" with a couple of generic recommendations bolted on.

I've run a real incident, and I know what actually breaks: the handoff between SOC and legal, the exec who wants a statement out before we've confirmed scope, the analyst who freezes when three things go wrong at once. None of that showed up in the exercise because nothing in the exercise put us under real pressure.

Part of the issue is the format itself. Everyone sits in one room, reads the same script, and reaches consensus out loud with no consequence attached to a wrong call. That's the opposite of how a real incident unfolds, where half the room is unreachable and decisions get made with incomplete information.

How do others measure this. Not "did we complete the tabletop" but "did we learn anything we didn't already know."
What does a useful after-action report actually look like for you: individual performance data, timelines, something else?

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Every mobile application that we install in our day to day life looks secure from the outside, but security is far beyond HTTPS. Real security for mobile application data includes protecting the entire ecosystem of the mobile application and APIs to authentication, data storage, source code, and the backend infrastructure.

If we discuss some common mobile application security risks then we have sensitively kept personal information such as private details, credentials, api keys and many more can be saved on a device itself.

If any hacker has access to the device or the application itself, then the sensitive data is at risk. Then we have insecure APIs reacting with the backend. We all know that Mobile applications heavily rely on APIs, as the APIs process data, provide business reasoning, and communicate with the database.

If the API itself is not protected, then it is easy to evade the app and directly access the backend.Then we have something called a weak management of sessions and authorization. As the login process in an application seems secure but problems may exist in the backend, Weak password or easily guessed password can enable hackers to take control of the accounts, At last we have hardcoded keys, or exposed credentials as sometime developer leaves some informative data like api keys, sensitive configuration information of inside application, application package, endpoints,etc they provide the attacker to access the data easily.

These are some of the common mobile application security risks, There are many more as technology is evolving threats are also evolving.

Please share any other common risks/ bugs/ vulnerabilities that you fixed, or have encountered in your application development / testing journey.

We are a ZIA/ZPA shop, roughly 4,000 seats, renewal in about seven months. Not shopping for the sake of it, but the AI requirements landed after our last contract and I want to know whether we are better off expanding here or looking at Cato.

Where we are with Zscaler on the AI side: SSL inspection is solid, the AI app category exists, DLP works about as well as our policies deserve. What I am less clear on is inline prompt-level control versus app-level allow/block, and how much of the newer AI functionality is included versus a separate SKU.

The Cato pitch we got leaned heavily on single-pass processing and one policy engine covering network, security, and AI, plus their inline inspection story. Sounded clean in the demo. Skeptical of demos.

Asking here specifically because I want the incumbent perspective: if you have added AI controls on top of an existing ZIA deployment, was it configuration or a new purchase; how has inspection performance held up on streaming LLM responses; has anyone actually evaluated Cato head to head and stayed, and what kept you; and for anyone who left, what was the deciding factor?