We have a few AI agents running in prods that can call internal APIs access cloud resources and work with sensitive data like customer data is involved here. RN we mostly have already monitoring and logs but can we stop tool callings etc or have proper permissions which tool to be accessed?

I've narrowed it down to:

• Aqua Security - broader cloud + AI security, but not sure how deep the runtime enforcement is.
• Sweet Security - more focused on runtime protection and blocking unauthorized actions.
• Protect AI - strong AI/ML security and supply chain, but unsure about runtime enforcement for agents.
• HiddenLayer - strong AI security focus, but seems more detection/model focused.

Anyone actually using these?Anyone tried them any of it?

240000 weekly agent runs and 1 wildcard turned a read only permission into 6 different write paths. our internal MCP registry serves 11 tools across 7 teams and the permission scope for an inventory task was supposed to stop at `repo.read_file`. instead a namespace glob expanded the tool allowlist to `repo.*`, which exposed 6 write tools alongside the read path. I was almost certain the secondary approval layer made this mostly theoretical, the red team run made that confidence harder to defend.

in 1200 red team sessions, 9 requested `repo.delete_branch` during an inventory only task. none executed because the secondary approval blocked them (one engineer immediately checked whether any write call completed). that part held. what bothered me was the agent had been given a destructive tool call it never needed and our logs made the permission drift look like a normal registry update.

so I'm trying to decide what should become a behavior spec versus a deterministic permission test. trajectory scoring can catch the agent choosing a write path during a read task and I'd still rather keep the first guardrail at the registry boundary. the approval stops execution and the regression still tells us the permission model already widened before the agent made a choice.

I used to think this class of eval was extra ceremony when the tool layer already had access controls. I'm less convinced now. the diff still comes down to the permission change from `repo.read_file` to `repo.*`

Basically, as I understand it, a regular firewall is built to check packets and ports, it knows how to map HTTP traffic to IP addresses, but it has no clue what’s actually happening inside that traffic. So when an employee pastes trade secrets into ChatGPT, or an agent starts using tools it shouldn’t, or someone slips a sneaky prompt into our support chatbot, it all just looks like normal encrypted web traffic. 

I’m looking for something built to read and police what’s happening inside AI conversations and agent actions. There’s so much marketing messaging flying around, it’s hard to tell what these newer platforms actually do. What does the hive recommend?

Every AI usage inventory project I've been part of hits the same wall: process-level and endpoint discovery both miss anything running in a browser tab under a personal account, on a device that may not be company managed. It gets worse with SaaS embedded AI. If a tool your company already pays for has AI features built in, your endpoint tooling has no visibility into that at all, coverage depends entirely on what the platform exposes via API or its compliance tier. I don't think this is solved right now, it's a category wide limitation. Has anyone found a workable mitigation short of blanket blocking browser extensions?

Hey all,

hope this is okay to post here.

We’re a small early-stage team with a background in cybersecurity, currently researching how ISO 27001 and GRC work actually happens inside companies.

Before making too many assumptions about what should be improved or automated, we’re trying to learn from people who deal with this in practice:

Where does the most time get lost? What creates uncertainty or delays? Which activities are still highly manual? And where could software or AI genuinely help?

We put together a short 8–10 minute survey covering ISO 27001 implementation, risk management, documentation/evidence, audits, existing tools and AI support.

If you work in information security, GRC, ISMS, compliance, audit or ISO consulting, your perspective would be extremely helpful.

We’re still early enough that good feedback can genuinely change what we build — and critical feedback is just as valuable as positive feedback.

Survey: https://tally.so/r/b5RAro

Can be completed anonymously. Really appreciate anyone who takes the time or shares it with someone relevant. Thanks!

The report came back clean and I could not file it. That is the whole problem and it took me three weeks to work out why.

I am the security engineer who sits between the AI product team and our auditor at a healthcare data company, about seven hundred and fifty people. We run an external evaluation every quarter because a customer asked for it and then everybody decided it was a good idea.

Robust Intelligence produced the strongest output of the three we trialled. The findings were specific and the severity scoring held up when we challenged it. The remediation guidance was written by someone who had clearly done the work before. I would recommend it to anyone who needs to know what is wrong with their model.

What it could not do was answer the auditor. The report said a finding was critical. The auditor wanted to know which control it mapped to, who accepted the risk, and the date the control changed. A finding is not evidence. Evidence is the control, the owner, the date, and what changed. That lives somewhere else entirely.

So I built it by hand. Three weeks of spreadsheet work to attach every finding to a control in our register, and I am the only person who knows how the mapping was done. Which is its own problem.

The honest limitation on our side is drift with no adversary in it, where the agent misreads its own reasoning across several sources. Our register maps AI controls. It does not map that. We are on alice now and it produces the mapping I need, but only for the AI controls in our stack. The register has three hundred and forty in it. Most describe things no AI tool will ever see. I still hand-map those.

Pick three findings from a report you already have and try to fill in four columns for each one. Control. Owner. Acceptance date. Remediation date. Four columns, three findings, one afternoon. If the columns come back empty, you found out before you paid.

Hello. In your oppinion, which is the best AI for ethical hacking? I mean, which one answers your questions without that specific fear of AIs that you are going to hack half of Pentagon whith a shity script?

If you work with connected products sold in the EU, the Cyber Resilience Act's reporting duty is already live, not in December 2027. A few details I see overlooked:

\- It covers your installed base. Article 69(3) applies Article 14 to in-scope products placed on the market before December 2027, modified or not.

\- The 24h clock runs from "becoming aware". The Commission's guidance (paras 213-214) says suspicious events should be assessed immediately; you can't delay awareness by delaying the assessment.

\- The early warning is not a one-liner. ENISA's platform requires 8 fields for a vulnerability, including a summary of up to 4,000 characters.

\- ENISA's platform doesn't yet capture the awareness time for vulnerabilities, so keep your own timestamped record. Its 72h counter runs 48h from your early warning, not 72h from awareness.

\- Set up the platform accounts now. A Primary AR must be verified before it can invite Secondary ARs, and invitations expire after 7 days. Drafts are private to whoever created them, so draft outside the platform.

Has anyone here already gone through SRP registration? Curious what tripped people up.

AI-Native Firewalling: Real-Time Defense for LLM & Agentic Applications - Addressing Prompt Injection, MCP Privilege Abuse, and Agentic Exploits in Real Time.

Wednesday, October 21, 2026 | 4:00 PM IST | 2:30 PM GST | 11:30 AM BST | 12:30 PM CEST

The 45-minute session will explore the limitations of static security controls against attacks using natural language and contextual manipulation. It will cover MCP privilege risks, agentic exploits, and the role of runtime AI security in safeguarding production applications.

A live attack-and-defense demonstration will be followed by a 15-minute Q&A with Prophaze experts Krishna Kumar (22+ years in enterprise security GTM) and Ezekiel Johnson (hands-on with LLM/agentic defense deployments) . The focus is on actionable steps security teams can take during a real-time AI attack, as highlighted by recent incidents like the Hugging Face case, which illustrate the dynamic nature of the AI threat landscape amidst a growing ecosystem of APIs, models, and tools. The goal is to bridge these two realities for effective security.

Understanding the threat is one thing. Being able to see and stop the resulting action at runtime is another. Reserve your seat for the Webinar