matrixgard

It is different from BI and the gap is worth naming. BI connects deterministic queries from a known operator schema. An agent is an unbounded query generator, so your risk surface includes both what it asks for and what it synthesises from what it gets back. RLS alone won't help you, because the moment the agent gets handed a view it is already reasoning over rows you wanted to protect.

The practical answer I've seen hold up: push column-level redaction before the agent layer, not after. A logical replica with PII columns replaced by null or a stable hash gives you the same query surface, plus one audit row per session at the connection pooler, not the app. The AI team gets its data, you get a hard boundary that doesn't rely on prompt engineering.

The harder question is ownership. In a startup where the ML lead, the infra person and the security person are often the same tired CTO at 10pm, the right answer depends on who gets paged when a hallucinated query wakes up the primary. Usually the answer is nobody, which is the real problem behind the technical one.

$34k in 8 days with zero users is the flavor of bug that makes CFOs distrust engineering. The thing that would have caught this: anomaly detection scoped to the service + the account, not just the total bill. Most teams monitor the aggregate and only spike-alert above some threshold — by then it's 4 days old. Per-service p95-vs-median alerts at hourly granularity would have flagged this inside 6 hours. Cloudflare should absolutely ship platform-side guardrails here, but until they do, self-built alerts at the service level are the only real defense.

eight months is usually when the first version of "what you built" stops working and you have to build the thing customers actually want. the gap between those two is what most people call "0 revenue growth."

we went through something similar building a fintech tool — spent months on features nobody asked for because we assumed we understood the problem. what actually changed it: stopped building for what we assumed and started building for one specific thing one customer said they'd pay for that day. just one. then the next.

what was the specific use case you landed on?

eight months is usually when the first version of "what you built" stops working and you have to build the thing customers actually want. the gap between those two is what most people call "0 revenue growth."

we went through something similar building a fintech tool — spent months on features nobody asked for because we assumed we understood the problem. what actually changed it: stopped building for what we assumed and started building for one specific thing one customer said they'd pay for that day. just one. then the next.

what was the specific use case you landed on?

Static IP whitelisting is a nightmare in practice -- we ran it for about 8 months and the support burden was basically someone's part-time job. Every time someone's hotel or coffee shop rotated IPs, they'd open a ticket. We moved to AWS SSM Session Manager a couple years back and haven't touched a bastion since. No open ports, no SSH keys to rotate, and `aws ssm start-session --target i-xxxxxxx` just works from anywhere as long as they have valid IAM creds. CloudTrail picks up the session automatically, which was a side benefit we hadn't even planned for.

IAM Identity Center (was just called SSO before the rename) with a short session duration -- we do 8-hour max with MFA at login -- handles the traveling employee case cleanly. They re-authenticate, no ticket. Overhead compared to running a VPN server you have to patch is basically zero.

The one thing that bit us: we kept a bastion sitting around "just in case" for way too long before we cleaned it up. It was live for almost a year after we didn't need it anymore. What's the main access pattern you're trying to solve -- DB access, SSH to EC2, or something different?

Deliberately is doing a lot of work in that sentence and it's exactly right. The shortcuts that kill you aren't the ones you knew were shortcuts it's the ones that felt like reasonable decisions under pressure and only look like shortcuts in hindsight.

The "isolate the risk" part is where most early CTOs underestimate the blast radius. A shortcut in the auth layer feels isolated until six months later when three other systems are built on top of assumptions it makes. By then it's not a shortcut anymore, it's load-bearing technical debt.

What's the recovery actually looked like in practice when you've caught it early enough — do you get clean rewrites or is it mostly containment?

The 20% contamination number on ClawHub was genuinely alarming -- at that scale it's not opportunistic, it's systematic. The multi-pass approach makes sense given how trivially obfuscated payloads evade single-regex scanning; same problem npm has been fighting for years where a base64 decode or dynamic require wrapper kills most static analysis.

One thing worth thinking about beyond detection: even a perfect scanner at install time doesn't protect against skills that start clean and phone home post-install. The runtime layer is a different problem -- restricting what a skill process can actually touch (outbound network, credential paths, filesystem writes outside its own dir) probably matters as much as the intake scan. Seccomp or at minimum per-skill network namespacing would close that gap.

Did any of the 824 original malicious skills survive all 6 passes, or were they each caught by at least one detector?

The vault/proxy layer solving the "2am paste" vector but not the semantic leakage is exactly the gap most teams don't account for. Ephemeral key naming, endpoint patterns, TTL behaviors -- all of this is in the training corpus and no amount of runtime secret rotation changes what the model already internalized. You've essentially found that your threat model stopped at input hygiene but the model itself is a side channel.

What I'd add to the defense stack: structured output validation that flags known internal naming patterns before they reach the client, plus anomaly detection on response metadata -- token budget shifts, refusal rates, response shape changes under semantic pressure. At 75% convergence you're past "interesting research" into "reliable extraction technique," which means you need a detection layer, not just prevention.

Have you tested this against non-OpenAI models like Claude or Gemini to see if the naming bleed is GPT-4o-specific or a broader training corpus problem?

The proxy approach in the other comment handles the technical control side well. The harder part is the auditor question you slipped in at the end — that one trips up almost every team I've talked to. Most companies cannot produce a log showing which employee sent what data to which model, when it was authorized, and what classification level it had. The governance infrastructure for AI just isn't there yet, and auditors are starting to catch up.

SOC 2 and ISO 27001 auditors are now explicitly asking for evidence of data flow controls around AI integrations. "Policy says don't paste customer data into ChatGPT" gets zero credit as a control. What they want to see is technical enforcement with logging — something you can point to and say "here is the record of every outbound prompt from the last 90 days, here is what was flagged." If you can't produce that, it's a finding.

The other gap that's easy to miss: most teams don't know what's sensitive until it's already in a prompt. The classification problem comes before the proxy. What are your teams actually pulling into their prompts right now — internal docs, support tickets, code with credentials, database outputs? That's usually where the real exposure surfaces.

A breach at this scale almost never comes from a single access event — moving a petabyte takes time, and that kind of sustained egress usually means either the detection tooling wasn't watching outbound data flows, or alerts fired and got buried in noise. "1 petabyte" from a hacker claim is probably inflated, but even 5-10% of that is catastrophic depending on what's in it.

What's worth paying attention to here is that Telus Digital is a BPO/outsourcing company, which means the blast radius almost certainly extends to their clients. If your company has API integrations with Telus Digital, or gave them any kind of federated access to internal systems, now is the time to audit what they held and rotate anything they could have touched. Downstream credential exposure in third-party breaches is consistently underreacted to.

The employee data angle is also interesting that usually means developer workstations and internal tooling were in scope, not just the customer-facing layer. Makes the "how did detection miss it" question even harder. Does anyone know if Telus Digital ran a shared SOC across their outsourcing clients, or was each client siloed?

The part that doesn't show up in these posts is what happens when the AI-generated code meets real users. Works fine in dev, clean in staging, then production throws an edge case the model never saw and you're staring at a 3am incident with no mental model of what's actually running.

Vibe coding is genuinely useful for prototyping — I use it. But there's a difference between using AI to move faster and outsourcing your understanding to it entirely. The second one catches up with you the moment something breaks and you have no instinct about where to look.

Curious what the actual product was — did it make it to users before things fell apart, or did it not get that far?

The thing most first-year CTOs don't see coming is the translation problem. You understand the system. The founder understands the market. And there's this gap where critical decisions get made based on whoever can communicate their uncertainty the most confidently.

I've seen it go wrong both ways — CTOs who gold-plate systems nobody uses yet, and founders who promise customers features that are a month away from being architecturally possible. Both happen because the two sides aren't seeing the same thing.

The best early-stage CTOs I've watched work well are the ones who treat "no we can't do that" as a last resort. What feature did you promise that you genuinely couldn't deliver, and what did you actually ship instead?

Twenty years of enterprise supply chain GTM and you're asking if you need a technical co-founder — the answer really depends on what phase you're in and how fast you need to move.

For where you are now (building on top of an existing AI platform, adding domain intelligence and decision workflows), you don't need a co-founder who can invent a new transformer. You need someone who can move fast, understand the product deeply, and ship without a lot of hand-holding. That profile is often a strong contractor or early hire, not someone who needs equal equity to stay motivated.

The co-founder model makes sense when there's genuine tech risk that only a senior technical person can see around — when you're building the infrastructure itself. You're not. The risk you described is clearly on the product and GTM side, which you already own. What's your current thinking on timeline to first deployed version with a real supply chain customer?

The EPHEMERAL_KEY pattern here is interesting but the deeper issue is the workflow that creates this. Teams pasting real credentials into LLM prompts to debug auth errors is probably more widespread than anyone wants to admit — it's the path of least resistance when you're getting a 401 at 2am. The model leaking what it was trained on is a symptom; the root cause is no secrets rotation policy and no sanitization step before anything hits an AI API.

What I've seen work is treating LLM API calls like you'd treat external logging — strip or redact anything that looks like a credential before it leaves your process. A simple regex on the request payload costs almost nothing and catches the lazy-paste case.

Are you seeing this as a widespread pattern in your testing, or did this surface from one specific integration?

The last-mile stall is real and the security piece is usually what tips it from "almost there" to "never shipped." Environment variables and secrets is where I see the most shortcuts — things like hardcoded keys in the repo that worked fine locally, or a .env file that accidentally got committed because no one set up a proper .gitignore for the framework Cursor generated.

The CI/CD gap matters a lot too. AI-generated code tends to skip the boring scaffolding: no branch protection, no secret scanning in the pipeline, no way to roll back safely if something goes wrong in prod. That stuff is invisible until it isn't.

What's the actual blocker for the projects you're seeing stall — is it the infrastructure setup itself, or is it more that the founders aren't confident the code is production-safe?

Running an AI agent with whatever credentials happen to be in the shell is basically the same mistake as running your app as root — feels fine until the agent makes a bad decision or gets manipulated. On a typical dev machine that's a personal AWS profile with admin access; on prod it's usually whatever the CI service account can touch, which is often a lot more than it should be.

The CI integration is the piece I'd actually lean on first. Most teams I've seen think about agent access controls after they've already deployed, at which point you're doing cleanup instead of prevention. Gating it in the pipeline means the access question gets answered before the agent is running against your Terraform state and live kube contexts.

Are you seeing any patterns in severity distribution — mostly cloud creds coming up critical, or are the kube context exposures landing higher than expected?

The lekt9/foundry case that rodchalski flagged is the one I'd lose sleep over. Static analysis, AI audit — it doesn't matter, you can't catch what isn't written yet. That's a fundamentally different threat model than what most security tooling is designed for.

The closest parallel I've seen in practice is OAuth scope creep from a few years back — teams installing third-party integrations with broad permissions and never reviewing them. At least those had a permission dialog and an audit log. Agent skills install with one command and the full attack surface is whatever the agent can do in your shell, including your cloud credentials and prod contexts.

What's your signal on whether the malicious installs are actively being exploited or mostly sitting dormant? Wondering if there's any telemetry on runtime execution vs. just install counts.

The Woflow situation is a textbook third-party risk scenario that keeps playing out — a mid-size SaaS vendor holds data for enterprise customers, has fewer security controls than those customers would require of themselves, and becomes the weak link. ShinyhHunters specifically targets vendors like this because the breach-to-data ratio is favorable.

What makes vendor breaches particularly painful to respond to is that your incident response playbook doesn't really apply. You can't isolate the affected system, you can't pull logs from their infra, and your customers are asking you questions you literally cannot answer for 48-72 hours. The only real leverage you have is contractual — SLAs around breach notification, security attestations, right-to-audit clauses — and most orgs don't negotiate those until after something like this happens.

If you're a startup that processes data through third-party SaaS tools, what's your current process for assessing vendor security posture before integration? Questionnaire-based, SOC 2 report review, something else?

The reverse SSH tunnel detail is what makes this genuinely alarming — not the crypto mining itself, but that outbound-initiated channels effectively null out your ingress controls. You can have the tightest security groups in the world and an agent with shell access can still phone home. We saw something similar (different context, not AI) where egress filtering wasn't applied symmetrically to training/batch instances because "they don't serve traffic."

The GPU compute diversion is also underreported as a cost signal. If you have any agentic workloads, you probably want anomaly detection on GPU utilization per job, not just billing alerts — by the time your bill spikes, the damage is already days old.

What runtime isolation are you seeing orgs actually deploy for agent workloads? gVisor, Firecracker, something else? Curious whether this is pushing people toward stronger VM-level boundaries or if network egress controls are the more practical mitigation.

The pattern you found maps to what I've seen too — most feedback exchanges are symmetric in the worst way: people who want feedback aren't giving it, so the people who could give it stop showing up.

The projects that get real feedback usually have someone who already has credibility in the space, so feedback feels like talking to a peer rather than being recruited as a tester. It's less about incentive structures and more about perceived mutual value.

What are you building? Sometimes the thread matters more than the mechanism.

The bottleneck isn't usually coding speed. Most of the AI-generated stuff that doesn't turn into useful software fails before the first line is written — nobody actually understood the problem they were solving.

The projects that do ship and matter tend to come from people who had a specific, painful problem and built exactly for that. The vaccine appointment site you mentioned is the perfect example — someone was solving their own family's real problem, not exploring what AI could generate.

What kind of software were you hoping to see more of? Curious if there's a specific gap you're noticing.

Multi-cloud Kubernetes at scale (AWS + Azure simultaneously) is one of those setups where IAM boundaries and secrets management tend to drift faster than the team realizes — each cloud has its own identity model and they don't map cleanly to each other, so the blast radius from a misconfigured service account is usually larger than anyone expects.

Worth doing a security pass on the existing setup before new platform engineers inherit it as the baseline. Distributed teams expanding across regions amplify this fast.

Happy to talk through the infra security side if useful — this is the kind of work I do with teams at this stage.

The stack you're describing (multi-cloud Terraform across AWS/GCP/Azure handling law enforcement evidence) is genuinely complex to secure from the ground up — especially with the compliance exposure that comes with CJIS-adjacent data.

One thing worth thinking about before the new hire starts: the first infra person at a bootstrapped company almost always walks into cloud credentials that are broader than needed and Terraform state that's never had a security pass. Getting a baseline done before onboarding gives them a clean starting point and avoids inheriting someone else's decisions as their first task.

Happy to talk through the infra side if useful — I work with teams at this stage on exactly this.

The CloudWatch + kubectl + 4-other-tools triage loop is real — I've burned hours on that exact workflow at 2am during an incident. The pain isn't the tools themselves, it's that each one gives you a different slice of truth with no shared context, so you end up mentally joining logs across systems with no guarantee you're even looking at the right time window.

The SOC2 compliance estimate piece is interesting. In practice that's notoriously hard to do from infrastructure signals alone — a lot of what auditors actually care about (access reviews, change management evidence, vendor risk questionnaires) lives outside the infra layer entirely. Curious how you're approaching that part — are you generating evidence artifacts or more of a gap-assessment output?

Also, how are you handling credentials for multi-cloud — are you asking users to drop keys in or is there an OIDC/role-based flow?

The SCIM provisioning piece is usually where it falls apart first. Even when both sides have Atlassian Access, the external org's IdP doesn't cleanly federate, so you end up with manually provisioned guest accounts that nobody deactivates when the contractor rolls off. Infosec says no to that, and they're right.

The harder part in defense/aerospace is data residency and CUI handling. Jira Cloud will never be on the approved list for anything touching export-controlled data, so you're back to Data Center on-prem — and now you're asking a prime contractor to open VPN tunnels to a supplier's DC instance, which their network team will reject outright. The "let's just use email" fallback isn't laziness, it's often the path of least compliance resistance.

What's the typical org size on the contractor side — are these large primes with mature IAM programs or mid-tier suppliers who barely have SSO themselves?

The skepticism-to-flow pattern you described is something that plays out almost identically across teams — the engineers who resist AI longest are usually the ones who are best at their craft and most aware of where the output is wrong. The flip happens when they stop treating it like a search engine and start treating it like a junior dev they have to review.

The productivity difference between teams who hit that click and those who don't is getting wide enough that it's becoming a hiring signal in both directions. The founders I see getting the most out of it are the ones who understand this enough to ask good questions of their team, even if they can't read the code.

What was the biggest surprise for you watching the experiment — was it which engineers clicked fastest, or something else?

The anchoring point is real and you've framed it well. Most people don't even realize the first suggestion wins 80% of the time, they just go along with it.

The transit data angle is the differentiator here — straight-line midpoints are useless in cities with asymmetric transit coverage. Curious how you're handling cases where one person is in a transit dead zone and others are on a good line. Does the fairness score penalize that person's zone or just present the raw reachability overlap?

Also the budget filter suggestion in comments is interesting — have you thought about layering in venue ratings so the "fairest spot" is also the best-reviewed option in the overlap zone?

The B2B-first advice in the comments is right, but the piece most teams skip is that physio clinics and rehab centers don't buy software, they buy outcomes. Your pitch to a clinic owner needs to show how this reduces their admin time or improves patient retention, not just that the AI is cool.

For regulated environments, getting 2-3 pilot clinics to use it for free in exchange for documented outcomes is worth more than any marketing spend right now. That data becomes your entire sales deck for the next 10 clinics.

What does your onboarding look like for a clinic that wants to assign it to patients — is that workflow already built out?

The Annex III vs general purpose distinction is doing a lot of heavy lifting here that most teams aren't thinking about yet. If your agent is making or substantially influencing decisions in employment, credit, education, or critical infrastructure, you're in the high-risk bucket with the full 25-item checklist. If it's general purpose AI (GPAI), you're under a different regime entirely with lighter obligations unless it's "systemic risk" tier. Getting this classification wrong in either direction is expensive — either you over-engineer compliance for something that doesn't need it, or you show up to an audit in August having built the wrong controls.

The part teams consistently underestimate is the human oversight requirement under Article 14. It's not just logging that a human was in the loop, you have to demonstrate the human had meaningful ability to override, and that the system was designed to make that possible. That's an architecture decision, not a documentation task, and you can't bolt it on after.

What's the actual deployment context here — are you shipping this to customers who then run agents against EU data subjects, or is this internal tooling?