Building Secure AI Systems with DevSecOps and AWS

Your AI agent just got compromised. And your IAM policy is why. Agentic AI is no longer a future concept. Teams are shipping autonomous systems that reason, retrieve, and act. But most of these deployments have a silent flaw baked in from day one. Wildcard permissions. No tenant isolation. Models talking directly to databases. No runtime validation. I've seen it, I've documented it, and I wrote the guide I wish existed earlier. 📖 "Designing Secure Agentic AI Systems on AWS" is now live on SUDO Consultants blog page. Inside I break down: -> How to implement zero-trust identity for AI agents, not just humans -> How to prevent cross-tenant data exposure in RAG pipelines -> How to add a runtime validation layer so the model can't take unchecked actions -> The enterprise deployment checklist before you go live The gap between "it works" and "it's production-ready" is a security architecture gap. Let's close it. Link in comments. #AgenticAI #AISecurity #AWS #Bedrock #ZeroTrust #MLOps #CloudArchitecture

Your AI agent has production access — but do you have the guardrails to keep it in check? Moving autonomous agents from RAG prototypes to production-grade infrastructure management takes more than a model. It takes a governance architecture. Here's how to secure your agentic workflows on AWS: 🔍 TACT Framework (Risk Assessment) Implement Traceability, Accountability, Consequences, and Trust boundaries to evaluate every action before execution. If you're using Strands Agents SDK, steering and hooks are a natural implementation layer for TACT — they let you inject traceability, access control, and trust boundaries directly into the agent's execution loop before any tool fires. 🛡️ Infrastructure-Level Data Protection Stop manually redacting. Use Amazon Bedrock Guardrails to intercept and sanitize sensitive data (PII/PHI) at the infrastructure level. Compliance by design, not by hope. 🕸️ Graph-Based Reasoning Security risks hide in relationships, not individual resources. Use Amazon Neptune to map infrastructure connections and catch complex multi-hop threats that traditional databases miss. ⏸️ Human-in-the-Loop Orchestration High-risk tasks deserve human judgment. Use AWS Step Functions to create durable, time-bound approval flows that pause agent execution until a human validates the intent. #AWS #GenerativeAI #CloudSecurity #AgenticAI #AmazonBedrock #StrandsAgents #InfrastructureAsCode #TACT

This is a strong validation of the momentum Dynatrace is building, with explosive growth, real customer impact, and leadership in AI-powered observability. Reaching this milestone shows how essential our platform has become. But it’s still clear we’re just getting started as enterprises move toward autonomous, self-healing systems. We’re excited to deepen collaboration with partners to extend these innovations and deliver even more powerful, outcome-driven solutions for customers. https://lnkd.in/gbYQAsQ3 #Dynatrace #AIObservability #PartnerEcosystem #DigitalTransformation #NYSE #AI #Observability

✨ Future-State #Observability Architecture ✨ The future observability ecosystem is not just about monitoring—it’s about intelligent, unified insight across the enterprise. This vision is built on five tightly integrated architectural layers: 🔹 Telemetry Foundation Vendor-agnostic, OpenTelemetry-native data collection across infrastructure, applications, APIs, and AI workloads—flowing through a unified, cost-optimized pipeline. 🔹 Unified Observability Platform Full-stack visibility in a single pane of glass, with auto-generated service maps and business transaction tracing for all stakeholders—from ITOps to FinOps. 🔹 AI Intelligence & AIOps ML-driven analytics that detect anomalies, predict failures, identify root causes, and trigger automated remediation—augmented with business context. 🔹 Security & Compliance Fabric A shared telemetry backbone powering SecOps with enriched SIEM/SOAR/UEBA, enabling continuous Zero Trust compliance and automated evidence generation. 🔹 Agentic Control & AI Governance End-to-end oversight of AI agents and LLM workflows, with Guardian Agents ensuring safety, compliance, and human-in-the-loop governance. 👉 The shift is clear: from siloed monitoring to autonomous, context-aware, and governed observability. #Observability #AIOps #EnterpriseArchitecture #AI #DigitalTransformation #Cloud #Security #OpenTelemetry

Higress has entered the CNCF Sandbox, bringing unified Ingress controller and AI gateway capabilities built on Envoy and Istio. For enterprises deploying at scale, this means one less integration to manage between north-south traffic and AI workload routing. Standardization at the gateway layer is accelerating—and that's good for everyone. #Higress #CNCF #KubernetesGateway #AIInfrastructure #CloudNative https://lnkd.in/epyy5fDm

CIOs are signing off on agentic AI deployments. The telemetry infrastructure underneath them was built for microservices. That mismatch has a cost. Not just in dollars, though the ingestion charges on high-frequency agent workloads will get your attention fast. The real exposure is forensic: when an AI agent makes a bad decision at 2am, can you actually reconstruct what happened? Ghost Telemetry is what I'm calling the data that technically exists somewhere in your stack but was never indexed, routed, or retained in any useful way. It's the audit trail that's there and unreachable, not the one that was never collected. It's not a monitoring problem. It's an architecture problem that predates your AI rollout by years, and patching your existing stack won't close the gap. The blog breaks down what purpose-built agentic observability requires, and why most enterprises are going to hit this wall hard before they see it coming. https://lnkd.in/eNqMZjcW #Observability #SRE #DevOps #Apica #CloudNative

SurePath AI Deploys Real-Time Policy Controls for MCP Traffic 📌 SurePath AI just dropped MCP Policy Controls - a real-time security layer that stops AI agents from silently executing dangerous commands across enterprise tools like Salesforce or AWS. No more shadow AI sneaking in - this new governance tool inspects payloads at the protocol level, blocks risky actions before they happen, and lets admins enforce granular rules without disrupting operations. 🔗 Read more: https://lnkd.in/dFQnMXnr #Surepathai #Mcppolicycontrols #Modelcontextprotocol

AI Gateway in Databricks now extends Unity Catalog governance to agentic AI. As agents call LLMs, pull data through MCP servers, and invoke external APIs, every step touches sensitive data and audit requirements. With the latest updates to AI Gateway, you can now bring all of that into one governance model: • Set access policies once across every LLM and tool • Trace the full agent call chain end-to-end • Centralize logging for FinOps, engineering, and security https://lnkd.in/g4KZ_qPE

FinOps is asking why the AI bill jumped 40% last quarter. Engineering can't reproduce the agent failure from Tuesday. Security wants an audit trail for the board. Three teams. Three urgent questions. Zero shared answers. This is what "shadow AI" actually looks like in enterprise. Not rogue tools, but legitimate agents with no unified observability. Databricks AI Gateway solves this with one logging infrastructure that serves all three: ✅ FinOps gets dollar-level cost attribution by team, model, and environment. It's NOT just token counts ✅ Engineering gets full request/response payloads in Delta tables for root-cause debugging ✅ Security gets complete audit trails with identity, timestamp, and MCP call details And on top of that, you get automatic model fallbacks (Opus quota exhausted? Routes to Sonnet), rate limits by user or group, and MCP governance so agents can't access systems their users can't. This is what industrialising AI actually looks like and at Manuka ANZ | The Databricks People, we help you build a solid AI foundation so your agent value compounds over time. #aigateway #aigovernance #unifiedgovernance #databricks

AI Gateway in Databricks now extends Unity Catalog governance to agentic AI. As agents call LLMs, pull data through MCP servers, and invoke external APIs, every step touches sensitive data and audit requirements. With the latest updates to AI Gateway, you can now bring all of that into one governance model: • Set access policies once across every LLM and tool • Trace the full agent call chain end-to-end • Centralize logging for FinOps, engineering, and security https://lnkd.in/g4KZ_qPE