Tools to Improve Cloud Security

6 Steps to Reducing Your Cloud Cybersecurity Debt 1) Integrate security into the SDLC as early as possible. 2) Monitor your CSP security posture as well as the posture of your deployed assets. Recommend using a CSPM tool here like Wiz, Orca Security, or Prisma Cloud by Palo Alto Networks 3) Restrict access as you move from left to right towards products. Access tends to necessarily be permissive on the left end of development but should become more restrictive as you got to test/qa and then most restrictive as you get to production. 4) Reduce your attack surface. Mitigate commonly exploited misconfigurations and exploitation techniques while monitoring cloud infrastructure for vulns and anomalies. 5) Perform a cyber-threat profile assessment. Understand threats specific to your cloud architecture and the top security risks you face. 6) Pentesting (or better yet, continuous testing) This can help identify complex "toxic combinations" before attackers exploit them, and provide quantitative data to help measure the risk associated with your cloud assets. #cloud #cyber #security (h/t Dark Reading "Reducing Security Debt in the Cloud")

Why does 92% of cloud breaches start at the code layer? Among the 4 C’s of Cloud-Native Security — Cloud, Cluster, Container, and Code — the Code layer is the most vulnerable. Bugs and vulnerabilities originate here, even before anything is built. 𝐌𝐨𝐬𝐭 𝐂𝐨𝐦𝐦𝐨𝐧 𝐑𝐢𝐬𝐤𝐬 : RCE (Remote Code Execution): Lets attackers run code on your server. XSS (Cross-Site Scripting): Hijacks user sessions via browser scripts. SQL Injection: Pulls unauthorized data from databases. SSRF (Server-Side Request Forgery): Forces internal systems to leak data. Credential Hardcoding, Dependency Flaws, and Logic Bugs. If code is weak, the entire stack crumbles. This is why practices like 𝐋𝐢𝐧𝐭𝐢𝐧𝐠(code hygiene checks), Dependency Scanning (vulnerable library detection), and 𝐃𝐀𝐒𝐓 (Dynamic Application Security Testing) are critical. Among the major vendors out there; here is how Dynatrace and Sumologic helps: 𝐃𝐲𝐧𝐚𝐭𝐫𝐚𝐜𝐞’𝐬 𝐎𝐟𝐟𝐞𝐫𝐢𝐧𝐠 : Application Security Module: AI-driven detection of runtime vulnerabilities across production code and libraries. PurePath Tracing: Shows exactly which code and functions are executed — great for root-cause detection. Davis AI: Uses causal machine learning to detect anomalies in code behavior before breaches happen. Integration with DevSecOps Pipelines: Flags vulnerabilities early by integrating with CI/CD tools for scanning and linting. S𝐮𝐦𝐨𝐋𝐨𝐠𝐢𝐜’𝐬 𝐨𝐟𝐟𝐞𝐫𝐢𝐧𝐠 : Cloud SIEM: Real-time alerts for known and unknown threats Insight Trainer: Continuously learns to reduce false positives in threat detection. Copilot (AI Assistant): Helps analyze logs and surface code-layer security gaps. DAST and Dependency Scanning Support: Through integrations and log-based pattern detection during runtime 𝐓𝐡𝐞 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲: Both platforms help — tackle vulnerabilities early, as code is written or deployed. Dynatrace outperfoms in code tracing and runtime protection, while Sumo Logic leads in SIEM and log intelligence. They complement help close security gaps before they become breaches. Proactive investment in Observability and SIEM solutions is no longer an option, but a must. It helps, detect and mitigate code vulnerabilities early in the development process - drive significant cost savings and reduce the reliance on extensive Data Loss Prevention (DLP) solutions. According to a research by HackerOne; organizations could save up to 𝟑𝟎%, if they were to address code-level vulnerabilities early during development - a practice known as 𝐬𝐡𝐢𝐟𝐭𝐢𝐧𝐠 𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐥𝐞𝐟𝐭. Do you agree? Feel free to add your thoughts. #cloudsecurity #observability #loganalytics #applicationmonitoring #twominutedigest

🔐 Kubernetes Security Isn’t Optional — It’s Critical. Kubernetes is powerful, but without the right security practices, your cluster is an open target. Here are 5 security steps I focus on to keep EKS and K8s environments safe: 1. Image Scanning in CI/CD • Scan container images for vulnerabilities before pushing them to production (e.g., Anchore, Clair). 2. Locking Down the Control Plane • Secure the kubelet API, enable RBAC, and enforce certificate rotation. 3. RBAC & Least Privilege Access • Use Roles, RoleBindings, and service accounts to ensure no one (and nothing) has more access than needed. 4. Pod-Level Security • Apply Pod Security Policies (PSP) or its replacements, network policies, and restrict privilege escalation. 5. Runtime Threat Detection • Tools like Falco can monitor abnormal container behavior and block threats in real time. 🔔 Follow me for more Kubernetes & DevSecOps insights. ⸻ #Kubernetes #K8s #DevSecOps #EKS #AWS #CloudSecurity #RBAC #Helm #GitOps #DevOps #ContainerSecurity #Anchore #ArgoCD #InfrastructureAsCode #CloudNative #PlatformEngineering #CI_CD

AppSec is evolving as security teams spend insane amounts of time investigating findings detected by code and cloud security scanners—and Jit is a platform that caught my attention recently for what comes next. Jit's AI Agents automatically evaluate the real risk every scanner-detected finding introduces based on the runtime context of each issue (like whether they're exploitable or internet-facing). What is extra cool is that the agents also consider your internal security policies and compliance objectives, so prioritization is based on the business's requirements. A few other things that stood out to me: 🔹 Humans remain in the loop before any agentic action is taken, and security teams get transparency into each task agents take to evaluate vulnerabilities 🔹 Their use of Model Context Protocols (MCPs) enables you to bring in data from your existing stack to inform prioritization while executing actions to mitigate security risks 🔹 In addition to continuous triage, these agents are automating many other flows, like code reviews for developers, reporting, and compliance gap analysis Jit demonstrates that the potential for Agentic AI in AppSec is great and will help tackle longstanding systemic issues from the workforce, tooling, and risk reduction. We need more AppSec tools that cut noise and support real-world decision-making. Jit looks like a strong step in that direction. #AppSec #DevSecOps #AI #CloudSecurity