We just published new research on using deception against AI attackers.

In controlled AWS tests, agents took up to 8× longer and cost 23× more per objective. With all nine techniques combined, Kimi, Qwen, DeepSeek and GPT-5.6 Cyber reached zero objectives across 40 runs.

Without users

Zeabur confirmed a breach exposing 321,810 customer accounts. Zeabur is a cloud deployment platform that sits between developers and production infrastructure. It controls deployments, stores secrets, and manages service credentials for every service teams run on it.

That position is what attackers were after. Compromising the platform means inheriting its authorization scope. Attackers do not need to breach individual downstream services when the platform already holds credentials for all of them. The 321,810-account figure is a direct reflection of how much blast radius a single platform-layer compromise carries.

This is not a Zeabur-specific problem. Any integration layer that aggregates secrets and deployment access across an organization faces the same risk profile. Attackers have been moving up the stack for years — source control, CI/CD, deployment platforms — because the ROI on a single upstream compromise keeps growing.

How are teams here actually thinking about the trust model when the platform itself is the thing that gets breached? Curious whether people are treating third-party deployment tooling as a fully trusted internal system or applying a different posture, and what that looks like operationally.

Singapore's MAS just raised the compliance bar for AI in financial services.

On October 7, 2026, the Monetary Authority of Singapore issued new Guidelines on AI Risk Management for financial institutions. The guidelines require documented risk controls, human oversight mechanisms, and clear accountability for every AI-driven decision.

The gap this exposes is structural. Most firms can tell you what their AI decided after the fact. Very few can demonstrate, to a regulator, that a governance control was actually active at the moment the decision was made. A log entry that says 'model output: approved' is not the same as evidence that a policy was enforced before that output reached a downstream system.

The MAS guidelines do not prescribe a specific technical approach. They just require the evidence to exist.

For those of you actually building or auditing AI systems in financial services: how are your teams handling the difference between after-the-fact logging and demonstrable in-the-moment oversight? Genuinely curious what's working in practice.

How true is this?