Secure AI adoption with data loss prevention (DLP)

Build a multi-agent supervisor pattern on OpenShift

A detailed walkthrough of implementing a multi-agent supervisor pattern on OpenShift using OpenShell sandboxes to enforce least-privilege isolation for AI agents. The pattern addresses the security risks of a single AI agent holding credentials for multiple systems (Prometheus, Loki, Confluence) by splitting work into specialized agents, each running in its own sandbox with kernel-enforced network policies (Landlock, seccomp). The architecture uses Claude as the orchestrating brain in Anthropic's cloud, while actual data access and reasoning happen inside per-agent OpenShell sandboxes on the cluster. Each sandbox gets a scoped network policy allowing only the specific binary to reach specific endpoints, limiting blast radius if any agent is compromised via prompt injection. The post includes a full incident-response demo showing parallel agent dispatch, per-agent policy enforcement, and result synthesis without raw PII ever leaving the cluster.

How Atlassian and Dropbox are driving effective AI transformation

Atlassian's Chief People and AI Enablement Officer and Dropbox's VP of People Operations share lessons from enterprise AI transformation. Both companies argue AI adoption fails when treated as purely a technology initiative rather than a cultural one. Key insights include: documentation-rich, async-first cultures have a natural advantage for AI adoption; measuring 'super users' (those using advanced AI features 40+ times/week in role-specific workflows) matters more than raw adoption rates; and practical frameworks like AI Working Agreements, AI Retros, and company-wide hackathons help build experimentation culture. About 30% of Atlassian staff qualify as super users today, and 99% use Rovo daily.

Frontier AI has broken the old rules of cyber defence, warns Palo Alto CIO

Palo Alto Networks CIO Meerah Rajavel warns that frontier AI models — including Anthropic's Mythos and OpenAI's GPT-5.5-cyber — can chain previously ignorable small bugs into novel, working exploits and generate attack code at scale. This renders traditional severity-based patching strategies obsolete. She expects these capabilities to be widely available within three to five months, urging organizations to adopt AI defensively now since adversaries already are. She also highlights elevated risk for African organizations, advocates for zero-trust architectures, and cautions against granting AI agents excessive permissions. Palo Alto's own SOC processes 90 billion events daily, with ~5,000 AI models reducing these to ~75 actionable alerts.

TotalSecure helps business adopt AI without the security handbrake

iqbusiness promotes TotalSecure and TotalSecure AI, two managed security services designed to help enterprises adopt AI without compromising data security or regulatory compliance. The piece highlights risks of uncontrolled AI use — such as employees uploading sensitive data to public AI platforms or shipping AI-generated code with hidden vulnerabilities. TotalSecure AI integrates with Microsoft Purview DSPM, Microsoft Sentinel, and Microsoft Defender XDR to provide continuous data classification, policy enforcement as code, real-time risk monitoring, and regulator-ready reporting. Claimed results include 30% faster AI adoption projects, 40% reduction in manual compliance reporting, and faster detection of data-leakage incidents.

How We Brought Enterprise-Managed Authorization to Rovo MCP with XAA and ID-JAG

Atlassian tích hợp cơ chế ủy quyền doanh nghiệp cho Rovo MCP thông qua XAA và ID-JAG, cho phép quản trị viên doanh nghiệp ủy quyền tập trung cho client MCP (như Claude) mà không cần người dùng xác nhận OAuth riêng lẻ. Hệ thống hoạt động bằng cách IdP doanh nghiệp phát hành ID-JAG assertion đã ký, sau đó Atlassian OAuth xác thực và đổi lấy token truy cập native, giải quyết thách thức về xác thực, nhận diện người dùng cross-domain và quản lý chính sách. Tính năng này đang trong giai đoạn beta riêng tư và mở rộng triển vọng cho các sản phẩm AI agent vượt qua ranh giới tin cậy ở quy mô lớn.

Lập trình viên phát triển ứng dụng AI hoặc tích hợp hệ thống cần hiểu cách triển khai các cơ chế xác thực và ủy quyền enterprise để xây dựng giải pháp an toàn, hiệu quả khi kết nối với các dịch vụ bên ngoài mà không cần yêu cầu đồng ý OAuth mỗi lần sử dụng.

Build a multi-agent supervisor pattern on Red Hat AI

A detailed walkthrough of implementing a multi-agent supervisor pattern using Claude Managed Agents, OpenShell sandboxes, and Red Hat AI/OpenShift. The pattern addresses the security problem of a single AI agent holding credentials to multiple systems by splitting work across specialized agents (metrics, logs, runbooks), each running in its own kernel-enforced sandbox with per-agent network policies. The architecture separates orchestration (Anthropic's cloud) from execution (on-cluster sandboxes), ensuring raw data and PII never leave the cluster. Each sandbox enforces least-privilege access via Landlock filesystem isolation, seccomp syscall filtering, and binary-scoped L7 network rules, limiting blast radius if any agent is prompt-injected.

AI made your people faster. But it’s your office that’s slowing them down.

As AI accelerates individual execution, the real bottleneck in knowledge work has shifted to coordination, alignment, and decision-making between people. Atlassian's research across 12,000 workers and 172 Fortune 1000 executives reveals a 'fragmentation tax' costing the F500 $1.61B annually. In response, Atlassian proposes a new office design framework built around three AI-era flow states: Ideate (generating ideas before prompting AI), Calibrate (rapid group decision-making on AI outputs), and Regulate (protected time for deep thinking and recovery). The framework also maps how AI's physical presence in offices will evolve from screen-based to voice-enabled to spatially ambient, and offers six design principles including clear AI presence signaling, voice-first acoustics, and treating wellbeing as performance infrastructure.