Automating Trust in Cloud Environments

📌 How to Build a Comprehensive Zero Trust Architecture on Azure Zero Trust means "never trust, always verify", no implicit trust for users, devices, apps, or networks, even if they’re inside the perimeter. A layered strategy combining strong identity, device compliance, adaptive access, network segmentation, runtime controls, and continuous monitoring helps you achieve true Zero Trust at scale. ❶ Strong Identity Control ◆ Use Microsoft Entra ID (Azure AD) to centrally manage human and workload identities. ◆ Enable MFA, Conditional Access, and risk-based sign-in to block suspicious logins. ◆ Automate access lifecycle and reviews with Entra ID Governance. ❷ Device Compliance Enforcement ◆ Manage devices with Intune to enforce compliance policies. ◆ Use Defender for Endpoint for real-time detection and automated response. ◆ Require healthy device posture before granting access. ❸ Adaptive Conditional Access ◆ Evaluate signals (location, device, session risk) before granting access. ◆ Block or require extra authentication dynamically. ◆ Reduce lateral movement by combining identity and device signals. ❹ Network Segmentation & Edge Protection ◆ Segment workloads with Azure Firewall, NSGs, and micro-segmentation. ◆ Use Application Gateway with WAF or Azure Front Door to protect against OWASP top 10. ◆ Leverage Secured Virtual Hub for centralized inspection and policy enforcement. ❺ Runtime & App Controls ◆ Use Defender for Cloud Apps to monitor SaaS and on-prem activity. ◆ Enable GitHub Advanced Security for code and supply chain protection. ◆ Apply Defender for Cloud runtime controls to containers, VMs, and serverless. ❻ Data Protection ◆ Use Purview to classify, label, and protect data. ◆ Encrypt data at rest and in transit; integrate Defender for Office 365 to block phishing. ◆ Manage privacy risk with Microsoft Priva. ❼ Continuous Threat Detection & Response ◆ Centralize detection and automation with Microsoft Sentinel. ◆ Use Defender for Cloud Secure Score and threat intelligence to improve posture. ◆ Automate remediation with playbooks. ❽ App & Infrastructure Hardening ◆ Enforce adaptive access for SaaS and on-prem apps. ◆ Extend security to multi-cloud and on-prem with Azure Arc. ◆ Use private endpoints and managed identities to eliminate secrets. ❾ API & Private Connectivity ◆ Use Defender for APIs to protect against common attacks. ◆ Expose APIs via App Gateway and APIM; block direct public access. ◆ Secure internal traffic with private links and internal DNS. ❶𝟎 Telemetry & Governance ◆ Monitor signals across identity, devices, networks, and apps. ◆ Track posture with Secure Score and automate compliance reporting. ◆ Use Just-In-Time access to reduce standing privileges. By combining these layers, you create an Azure environment that is secure, adaptive, and resilient, protecting all entry points and data without slowing innovation. #cloud #security #azure

Pattern Labs and Anthropic have published a highly detailed technical paper outlining how to protect both user data and model IP during AI inference using Trusted Execution Environments (TEEs). If you are building or deploying GenAI in sensitive environments, this report is essential. Key takeaways: • Describes two confidentiality models: protecting model inputs and outputs, and protecting model weights and architecture • Explains how TEEs provide security through hardware-enforced isolation and cryptographic attestation • Covers implementations across AWS Nitro Enclaves, Azure Confidential VMs, and GCP Confidential Space • Examines support for AI accelerators such as NVIDIA H100 using either native or bridged TEE approaches • Provides analysis of over 30 risks including KMS misconfiguration, supply chain compromise, and insecure enclave provisioning Who should care: • Cloud AI service providers offering inference APIs • Enterprises using LLMs to process sensitive or regulated data • Model owners deploying high-risk or frontier models with SL4 or SL5 confidentiality requirements What stood out: • Practical coverage of Bring Your Own Vulnerable Enclave (BYOVE) risks • Focus on reproducible builds and open-source auditability to ensure enclave integrity • Clear guidance on KMS design, model provisioning, and runtime isolation to prevent data leakage One action item: Use this report as a design and threat modeling checklist for any confidential inference deployment. Start by securing your enclave build process and verifying the trust chain of your model provisioning workflow. #ConfidentialComputing #GenAI #AIInference #LLMSecurity #TrustedExecution #ModelProtection #AIPrivacy #Anthropic #PatternLabs #SecureInference #ZeroTrust #CloudSecurity

As security engineers, we spend countless hours writing scripts, building dashboards, and chasing drift across fleets of EC2 instances and Kubernetes clusters, all in the name of “continuous compliance.” But what if instead of reacting to drift, we proactively queried our infrastructure the same way a language model queries a knowledge base? That’s the promise behind deploying a Model Context Protocol (MCP) server on AWS, a way to let AI agents securely ask “Is AIDE configured for host integrity?” or “Are EKS nodes enforcing FIPS-compliant ciphers?” and get structured, testable answers in real time. This isn’t about using LLMs to replace auditors. It’s about turning security questions into machine-verifiable actions: checking whether auditd is configured with immutable logs, confirming whether VPC microsegmentation rules align with Zero Trust, or ensuring CloudWatch is alerting on unauthorized config changes, all through declarative MCP interfaces. When deployed correctly, MCP could potentially become a middleware for security posture validation. On AWS, for example this means marrying IAM roles, signed task runners, and context-aware policies to let agents check config states without over-permissioning. Imagine an LLM automatically validating that a hardened AMI hasn’t diverged from your CIS/STIG baseline, or flagging missing log forwarding on a new K8s namespace. This is more than automation. It’s about turning security into a queryable surface, where evidence, not effort, drives assurance. 🔗 How to securely run Model Context Protocol (MCP) servers on the AWS Cloud using containerized architecture: https://lnkd.in/eiEhR527 🔗 Guidance for Deploying Model Context Protocol Servers on AWS: https://lnkd.in/er6r6Pxw

Machine IAM is vast and thus difficult, but luckily we have a handy box of great tools, technology, approaches and framework to help us. They make what seems like an insurmountable challenge manageable. Let’s open that tool box and take a look: Authorization frameworks (AuthZen, OPA, XACML, and Cedar) offer fine-grained, access control. They separate authorization logic from code, enabling dynamic policy enforcement based on attributes about the user, action, resource, and environmental context. This makes it easier to define, maintain and scale consistent access controls across systems. Kubernetes Secrets & service accounts help decouple sensitive information like API keys, credentials and certs from application code and infrastructure configuration, or provide identities with dynamic tokens. PKCE and DPOP: PKCE stops attackers from stealing your authorization codes, making OAuth safer for apps. DPoP locks tokens to your device, so even if stolen, they can’t be reused elsewhere. Secrets management tools (AWS and GCP Secrets Manager, Azure Key Vault, CyberArk Conjur, Hashicorp Vault, OpenBao) provide a secure, centralized way to store and control access to sensitive information such as credentials, API keys, and certificates. They help organizations move away from hardcoded secrets and make it easier to manage secrets across a variety of environments. Secure Production Identity Framework for Everyone (SPIFFE) establishes a universal identity standard for workloads. It issues cryptographically verifiable identities, enabling workloads to securely authenticate with each other across clouds or data centers. SPIFFE removes the need for hardcoded secrets and simplifies zero-trust architectures by automating identity provisioning and rotation. Service meshes (Istio, Linkerd, Teleport) secure and manage service-to-service communication, automating discovery, credentials, and policy enforcement. They embed identity, authentication, and authorization into network traffic, allowing only trusted workloads to interact, while improving visibility and control in complex systems. Token exchange: Think of token exchange as a way to trade one set of credentials for another with just the right privileges for a given task. OAuth 2.0 Token Exchange allows applications to swap tokens, transforming an initial identity or scope into a new, tightly-scoped credential tailored for downstream systems. This minimizes risk by granting only the permissions needed, when needed, keeping your security posture nimble and auditable across complex cloud environments. Workload identity managers (Astrix, Clutch, Entro, Oasis, Token Security, Natoma): Manage legacy and static identities by discovering accounts, static keys, and various credentials. They track ownership, support identity lifecycle management, assist with some credential rotation, and help enforce security policies for these constructs. I’ll be writing more about each one of them. #MachineIAM #NHI #IAM

Why Automating Access Controls is a Necessity for Securing Cloud Infrastructure ⬇️ To effectively secure cloud infrastructure, it's essential to automate access controls. Key components include: Fine-grained Periodic Access Reviews: Regular audits of user access rights to ensure they align with current roles and responsibilities. It's important to have independent reviewers, not just direct managers, evaluate access needs. Activity Monitoring: Continuous surveillance of user activities to detect anomalies or potential security breaches. This is particularly important given the multiple entry points into cloud Cloud Infrastructure. Timely Risk Remediation: Swift action to address identified security risks. Integrating remediation processes with IT service management systems like ServiceNow can ensure efficient resolution. Audit-Ready Evidence: Maintaining comprehensive logs and reports that demonstrate the effectiveness of security controls. This includes tie-out reports that verify access changes have been implemented as requested.