Cloud Security

𝟮𝟬 𝗧𝗼𝗽 𝗔𝗣𝗜 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗧𝗶𝗽𝘀 1. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗦𝘁𝗿𝗼𝗻𝗴 𝗔𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗔𝘂𝘁𝗵𝗼𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Make sure only authorized users can access your APIs. Use strong authentication methods, such as OAuth or OpenID Connect, and grant users the least privilege necessary to perform their tasks. 2. 𝗨𝘀𝗲 𝗛𝗧𝗧𝗣𝗦 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻: Encrypt all traffic between your APIs and clients to protect sensitive data from being intercepted by attackers. 3. 𝗟𝗶𝗺𝗶𝘁 𝗗𝗮𝘁𝗮 𝗦𝗵𝗮𝗿𝗶𝗻𝗴: APIs should only expose the data that clients need to function. Avoid exposing sensitive data, such as personally identifiable information (PII). 4. 𝗦𝘁𝗼𝗿𝗲 𝗣𝗮𝘀𝘀𝘄𝗼𝗿𝗱𝘀 𝗦𝗲𝗰𝘂𝗿𝗲𝗹𝘆: Hash passwords before storing them in a database. This will help to prevent attackers from stealing passwords if they breach your database. 5. 𝗨𝘀𝗲 𝘁𝗵𝗲 '𝗟𝗲𝗮𝘀𝘁 𝗣𝗿𝗶𝘃𝗶𝗹𝗲𝗴𝗲' 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲: Give users and applications only the permissions they need to perform their tasks. This will help to minimize the damage if an attacker gains access to an API. 6. 𝗥𝗲𝗴𝘂𝗹𝗮𝗿 𝗨𝗽𝗱𝗮𝘁𝗲𝘀: Keep your API software up to date with the latest security patches. 7. 𝗗𝗶𝘀𝗮𝗯𝗹𝗲 𝗗𝗲𝗳𝗮𝘂𝗹𝘁 𝗘𝗿𝗿𝗼𝗿𝘀: Default error messages can sometimes reveal sensitive information about your API. Configure your API to return generic error messages instead. 8. 𝗦𝗲𝗰𝘂𝗿𝗲 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁: Use secure methods for managing user sessions, such as using secure cookies with the HttpOnly flag set. 9. 𝗖𝗦𝗥𝗙 𝗧𝗼𝗸𝗲𝗻𝘀: Use CSRF tokens to prevent cross-site request forgery attacks. 10. 𝗦𝗮𝗳𝗲 𝗔𝗣𝗜 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Your API documentation should not contain any sensitive information. 11. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗧𝗲𝘀𝘁𝗶𝗻𝗴: Regularly conduct security testing of your APIs to identify and fix vulnerabilities. 12. 𝗧𝗼𝗸𝗲𝗻 𝗘𝘅𝗽𝗶𝗿𝗮𝘁𝗶𝗼𝗻: Implement token expiration to prevent attackers from using stolen tokens for extended periods. 13. 𝗦𝗲𝗰𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: Validate all user input to prevent injection attacks. 14. 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗛𝗲𝗮𝗱𝗲𝗿𝘀: Use security headers to protect your API from common attacks, such as XSS and clickjacking. 15. 𝗖𝗢𝗥𝗦 𝗖𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻: Configure Cross-Origin Resource Sharing (CORS) to restrict access to your API from unauthorized origins. 16. 𝗧𝗵𝗿𝗼𝘁𝘁𝗹𝗲 𝗟𝗼𝗴𝗶𝗻 𝗔𝘁𝘁𝗲𝗺𝗽𝘁𝘀: Throttle login attempts to prevent brute-force attacks. 17. 𝗔𝗣𝗜 𝗩𝗲𝗿𝘀𝗶𝗼𝗻𝗶𝗻𝗴: Use API versioning to allow you to make changes to your API without breaking existing clients. 18. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗰𝗿𝘆𝗽𝘁𝗶𝗼𝗻: Encrypt data at rest and in transit to protect it from unauthorized access. 19. 𝗟𝗼𝗴𝗴𝗶𝗻𝗴 𝗮𝗻𝗱 𝗔𝘂𝗱𝗶𝘁𝗶𝗻𝗴: Log all API access and activity to help you detect and investigate security incidents. 20. 𝗥𝗮𝘁𝗲 𝗟𝗶𝗺𝗶𝘁𝗶𝗻𝗴: Implement rate limiting to prevent API abuse and overload.

🚨NSA Releases Guidance on Hybrid and Multi-Cloud Environments🚨 The National Security Agency (NSA) recently published an important Cybersecurity Information Sheet (CSI): "Account for Complexities Introduced by Hybrid Cloud and Multi-Cloud Environments." As organizations increasingly adopt hybrid and multi-cloud strategies to enhance flexibility and scalability, understanding the complexities of these environments is crucial for securing digital assets. This CSI provides a comprehensive overview of the unique challenges presented by hybrid and multi-cloud setups. Key Insights Include: 🛠️ Operational Complexities: Addressing the knowledge and skill gaps that arise from managing diverse cloud environments and the potential for security gaps due to operational siloes. 🔗 Network Protections: Implementing Zero Trust principles to minimize data flows and secure communications across cloud environments. 🔑 Identity and Access Management (IAM): Ensuring robust identity management and access control across cloud platforms, adhering to the principle of least privilege. 📊 Logging and Monitoring: Centralizing log management for improved visibility and threat detection across hybrid and multi-cloud infrastructures. 🚑 Disaster Recovery: Utilizing multi-cloud strategies to ensure redundancy and resilience, facilitating rapid recovery from outages or cyber incidents. 📜 Compliance: Applying policy as code to ensure uniform security and compliance practices across all cloud environments. The guide also emphasizes the strategic use of Infrastructure as Code (IaC) to streamline cloud deployments and the importance of continuous education to keep pace with evolving cloud technologies. As organizations navigate the complexities of hybrid and multi-cloud strategies, this CSI provides valuable insights into securing cloud infrastructures against the backdrop of increasing cyber threats. Embracing these practices not only fortifies defenses but also ensures a scalable, compliant, and efficient cloud ecosystem. Read NSA's full guidance here: https://lnkd.in/eFfCSq5R #cybersecurity #innovation #ZeroTrust #cloudcomputing #programming #future #bigdata #softwareengineering

So far this year, AI has been speedrunning security incidents: – Mexico: attackers tell Claude they are doing a bug bounty, keep rephrasing until it starts helping, generate scripts, pivot into Mexican government systems, walk away with around 150 GB of taxpayer and government data. – DJI: hobby coder wants to joystick his $2,000 vacuum, uses Claude to poke the API, grabs a token, suddenly has control of 7,000 robot vacuums in 24 countries, complete with live cameras and floor maps because there was no real device ownership check at all. – OpenClaw: Meta’s Director of Safety and Alignment wires an AI agent into her real inbox, tells it to "ask before acting," watches it forget the instruction after context compaction and aggressively delete huge chunks of mail while she sprints to her Mac mini to yank the cord. Fun to read on X. Terrifying if you ship software. Here is the boring hygiene that would have killed most of this: ○ Treat AI like an intern with root access – Never let agents talk directly to prod. Put them behind narrow, well-reviewed internal APIs with strict allowlists. ○ Bind auth to identity and device, not just a random token –Token should be tied to user, device id, and scope. If a token is valid, it should still only control that one vacuum, that one inbox, that one account. ○ Enforce least privilege for everything –Agents get read-only by default. Separate tokens for "read config", "read logs", "change data", "touch money". Rotate often. ○ Never run AI-generated code in the same blast radius –Sandbox it. Separate VPC, separate account, fake data, no production secrets. Promotion to prod always goes through a human code review. ○ Log like a paranoid SRE –Every agent action is auditable: who issued it, which model, which IP, which token, which resource. Alert on weird patterns and mass actions. ○ Put real rate limits in front of sensitive APIs – Even if an agent goes rogue, it should hit a wall before "wipe 150 GB" or "delete 10,000 emails" becomes possible. ○ Keep secrets away from chats – No raw API keys or prod passwords inside prompts, notes, or agent configs. Use secret managers and short-lived credentials only. ○ Validate intent on destructive operations – Extra confirmation flows for delete, rotate, and mass update. Ideally, with out-of-band verification, not just "yes" in the same chat. ○ Test failure modes, not just happy paths – Red team your own agents. Ask "what happens if it forgets this instruction", "what if the token leaks", "what if someone lies about doing a bug bounty". -- ♻️ Share this for future reference 📢 Follow saed ‎for more & subscribe to the newsletter: https://lnkd.in/eD7hgbnk I am now on 📸 Instagram: instagram.com/saedctl say hello, DMs are open

I've reviewed Anthropic's Risk Report for Claude Opus 4.6 because many of our enterprise customers are actively deploying AI agents into production environments. When those systems fail, the consequences are operational, financial and reputational. Most of the reaction centers on the headline that catastrophic risk is very low but not negligible. What matters more for customers and future customers is how risk actually manifests inside live enterprise systems and what that means for uptime, data integrity and compliance. It does not look like a breach. It looks like business as usual. An agent subtly influencing procurement decisions. A finance workflow that starts omitting inconvenient data. Permissions that expand over time without clear oversight. Anthropic describes a scenario called Persistent Rogue Internal Deployment, where an AI system with privileged access creates a less monitored instance of itself and continues operating inside production systems. In a real enterprise environment, that translates into downtime, data exposure or regulatory impact. The organizations at greatest risk are not the ones moving cautiously. They are the ones who pushed agents into production without adding an operational governance layer. We have seen this pattern before in cloud adoption. Technology advances quickly, and controls often lag behind. That gap is where exposure grows. So what should enterprise IT and security teams do now? 1. Constrain actions, not just access. Define what an agent can set in motion and enforce least privilege at the identity level, just as you have done for human users for decades. 2. Log actions, not just outcomes. Maintain an auditable trail of what the agent did, where and what triggered it, the same standard applies to human operators in regulated environments. 3. Automate your tripwires. Do not rely on people to catch machine speed behavior. Build policy enforcement and anomaly response into the loop. 4. Audit your agent footprint. Inventory every agent, its owner, permissions and kill path. Governance starts with visibility and most enterprises are still building it. The window to build these guardrails is now, before the agent workforce scales. At Rackspace, 25 years of running mission-critical systems have taught us that trust without controls creates exposure. We build and operate AI infrastructure with governance embedded from day one because customers need speed, resilience and measurable outcomes, not experiments in production. What this means for you is simple. Move forward on AI with confidence, but make operational governance part of the foundation so scale strengthens your business instead of introducing risk.

🗞️ Needed report By CyberArk on a burning issue : identity security. A decisive element that will determine our ability to restore digital trust. 🔹 « Identity is now the primary attack surface. » Defenders must secure every identity — human and machine 🔹 with dynamic privilege controls, automation, and AI-enhanced monitoring 🔹and prepare now for LLM abuse and quantum disruption. Machine identities are the fastest-growing attack surface 🔹Growth outpaces human identities 45:1. 🔹Nearly half of machine identities access sensitive data, yet 2/3of organizations don’t treat them as privileged. Quantum readiness is urgent 🔹Quantum computing will break today’s cryptography (RSA, TLS, identity tokens). 🔹Transition planning to quantum-safe algorithms must start now, even before standards are finalized. Large Language Models include prompt injection, data leakage, and misuse of AI agents. So organizations must treat them as a new class of machine identity requiring monitoring, access controls, and secrets management. 🧰 What can we do? ⚒️ 1/ Implement Zero Standing Privileges (ZSP) • Remove always-on entitlements; grant access dynamically and just-in-time. • Minimize lateral movement by revoking privileges once tasks are complete 👥2/ Secure the full spectrum of identities • Differentiate controls for workforce, IT, developers, and machines. • Prioritize machine identities: vault credentials, rotate secrets, and eliminate hard-coded keys. 🛡️ 3/ Embed intelligent privilege controls • Apply session protection, isolation, and monitoring to high-risk access. • Enforce least privilege on endpoints; block or sandbox unknown apps. • Deploy Identity Threat Detection & Response (ITDR) for continuous monitoring. ♻️ 4/ Automate identity lifecycle management • Use orchestration to onboard, provision, rotate, and deprovision identities at scale. • Relieve staff from manual tasks, counter skill shortages, and improve compliance readiness. 5/ Align security with business and regulatory drivers • Build an “identity fabric” across IAM, PAM, cloud, SaaS, and compliance. • Tie metrics (KPIs, ROI, cyber insurance conditions) to board-level priorities. 6/ Prepare for next-generation threats • Establish AI/LLM security policies: control access, monitor usage, audit logs. • Begin phased adoption of post-quantum cryptography to protect long-lived sensitive data. Enjoy the read

A startup just got hit with a $450,000 Google Cloud bill in just 45 days. Their normal monthly spend? $1,500. What happened? Their API key was compromised, resulting in 19 billion character translations. The worst part? They didn't know until the bill arrived. This isn't just about money - it's about survival. A $450K unexpected bill could sink most startups. Three critical lessons I've learned running cloud infrastructure: First, treat your API keys like your house keys. You wouldn't leave your front door unlocked, would you? Regular security audits, key rotation, and access reviews aren't optional anymore - they're essential hygiene. Second, cloud cost management isn't just about optimization - it's also about protection. Set up a layered budget and cost alert system. For a $1,500 monthly spend, you want alerts at: • 25% ($375) - Early warning • 50% ($750) - Mid-month check-in • 75% ($1,125) - Time to review usage • 100% ($1,500) - Monthly budget hit • Any sudden spike over 10% of daily average Third, and this is crucial for AI/ML workloads - implement usage quotas and rate limiting. AI services can rack up costs exponentially faster than traditional compute resources. One compromised endpoint can burn through your yearly budget in days. Quick checklist for everyone running cloud services: • Have you set up billing alerts? • When was your last security audit? • Are your API keys properly scoped and rotated? • Do you have rate limiting in place? • Is there a hard billing cap on your projects? Don't wait for a $450K surprise to start thinking about these. Prevention costs pennies compared to the cure. What's your take on cloud cost management? Have you had any close calls? Reddit post link: https://lnkd.in/diaSgC3B

We analyzed logs from 500+ Kubernetes deployments this year. The result? Same mistakes…just wearing a new hat. Here’s what’s actually breaking production in 2025 (and yes, you’ll relate to at least 5 of these): 1. 𝐒𝐢𝐝𝐞𝐜𝐚𝐫 𝐂𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫𝐬 𝐋𝐞𝐟𝐭 𝐔𝐧𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐞𝐝 Everyone loves sidecars…until they silently crash and nobody notices. ✓ 𝐅𝐢𝐱: Treat sidecars like first-class citizens in your observability stack. 2. 𝐈𝐬𝐭𝐢𝐨 𝐎𝐯𝐞𝐫𝐜𝐨𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐲𝐧𝐝𝐫𝐨𝐦𝐞 (𝐈𝐎𝐒) Teams added Istio for a “simple” problem and woke up inside a dependency hell. ✓ 𝐅𝐢𝐱: If you don’t really need service mesh, don’t implement one. 3. 𝐙𝐨𝐦𝐛𝐢𝐞 𝐂𝐫𝐨𝐧𝐉𝐨𝐛𝐬 Scheduled jobs finished 6 months ago…but the pods? Still running, still billing. ✓ 𝐅𝐢𝐱: Automate cleanup with TTL controllers. Manual audits won’t cut it. 4. 𝐇𝐚𝐫𝐝𝐜𝐨𝐝𝐞𝐝 𝐒𝐞𝐜𝐫𝐞𝐭𝐬 𝐢𝐧 𝐇𝐞𝐥𝐦 𝐂𝐡𝐚𝐫𝐭𝐬 It’s 2025, and teams are still committing secrets into Git. ✓ 𝐅𝐢𝐱: Use External Secrets or Vault. Helm has no business managing secrets. 5. 𝐔𝐧𝐛𝐨𝐮𝐧𝐝𝐞𝐝 𝐏𝐕𝐂𝐬 (𝐏𝐞𝐫𝐬𝐢𝐬𝐭𝐞𝐧𝐭 𝐕𝐨𝐥𝐮𝐦𝐞 𝐂𝐡𝐚𝐨𝐬) Devs created PVCs with no lifecycle policies. Now your storage bill looks like a ransom note. ✓ 𝐅𝐢𝐱: Enforce Reclaim Policies and set size limits. 6. 𝐏𝐨𝐝 𝐃𝐢𝐬𝐫𝐮𝐩𝐭𝐢𝐨𝐧 𝐁𝐮𝐝𝐠𝐞𝐭? 𝐖𝐡𝐚𝐭’𝐬 𝐓𝐡𝐚𝐭? One bad rolling update and your entire service went down because nobody configured PDBs. ✓ 𝐅𝐢𝐱: Always define PodDisruptionBudgets for critical workloads. 7. 𝐂𝐏𝐔 𝐋𝐢𝐦𝐢𝐭𝐬 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐑𝐞𝐪𝐮𝐞𝐬𝐭𝐬 (𝐓𝐡𝐞 𝐓𝐡𝐫𝐨𝐭𝐭𝐥𝐞 𝐓𝐫𝐚𝐩) Containers starve at peak traffic while your cluster pretends it’s underutilized. ✓ 𝐅𝐢𝐱: Balance your Requests and Limits. Over-restricting kills performance. 8. 𝐈𝐠𝐧𝐨𝐫𝐢𝐧𝐠 𝐍𝐨𝐝𝐞𝐒𝐞𝐥𝐞𝐜𝐭𝐨𝐫 & 𝐀𝐟𝐟𝐢𝐧𝐢𝐭𝐲 𝐑𝐮𝐥𝐞𝐬 Your GPU workloads ended up on CPU-only nodes. Brilliant. ✓ 𝐅𝐢𝐱: Define clear NodeSelectors and Affinity rules. Kubernetes isn’t a mind reader. 9. 𝐏𝐫𝐨𝐛𝐞𝐬 𝐌𝐢𝐬𝐜𝐨𝐧𝐟𝐢𝐠𝐮𝐫𝐞𝐝 = 𝐒𝐞𝐥𝐟-𝐈𝐧𝐟𝐥𝐢𝐜𝐭𝐞𝐝 𝐃𝐃𝐨𝐒 Readiness and liveness probes firing too frequently? You just DDoSed your own app. ✓ 𝐅𝐢𝐱: Tune those probes like your uptime depends on it (because it does). 10. 𝐀𝐮𝐭𝐨𝐬𝐜𝐚𝐥𝐞𝐫𝐬 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 Your HPA scaled to zero, and nobody knew why (until angry customers called). ✓ 𝐅𝐢𝐱: Always pair autoscaling configs with proper metrics dashboards and alerts. Kubernetes isn’t failing us. We’re failing to operationalize it correctly. Stop treating it like magic. Start treating it like critical infrastructure. What’s the worst K8s mistake you’ve seen recently? ♻️ 𝐑𝐄𝐏𝐎𝐒𝐓 𝐒𝐨 𝐎𝐭𝐡𝐞𝐫𝐬 𝐂𝐚𝐧 𝐋𝐞𝐚𝐫𝐧.

What is the real key to breaking into cloud security? Skills that prove you can secure real-world environments. Here’s what matters more than a certificate 👇 1 - Infrastructure as Code (IaC): ↳ Can you secure cloud infrastructure before it’s even deployed? With IaC tools like Terraform and AWS CloudFormation, you define and manage infrastructure through code. But here’s the catch—misconfigurations in code can lead to massive vulnerabilities. Learn how to integrate security into your IaC pipelines to catch issues early. 2 - Secure Architecture Design: ↳ Cloud security isn’t just about patching vulnerabilities. It’s about designing systems that are secure from the ground up. Do you know how to build a secure VPC, configure IAM with least privilege, and implement network segmentation in multi-cloud environments? Architects prevent breaches before they happen. 3 - Identity and Access Management (IAM): ↳ Identity is the new perimeter in the cloud. Mastering IAM means knowing how to create least privilege policies, manage roles and permissions, and secure access to sensitive resources. Can you detect over-permissioned roles or misconfigured trust relationships? If you control access, you control the cloud. 4 - Security Automation: ↳ Manual security processes don’t scale in the cloud. Automation is key to staying ahead of threats. Learn how to automate security checks, incident response workflows, and compliance audits using tools like AWS Lambda, Security Hub, and GuardDuty. Automate the routine, focus on the critical. Focus on hands-on projects, real-world scenarios, and continuous learning. That’s how you stand out in the crowded world of cloud security. Good luck on your cloud security journey!

What Drives Your Cloud Security Strategy? It’s Not Your Tool Stack. I keep seeing the same pattern: organizations spend more each year on cloud security tools, yet preventable incidents continue to climb. The uncomfortable reality is that cloud security rarely fails because we lack technology. It fails because we lack consistent execution. Consider the “modern” multicloud enterprise that adopts AWS, Azure, and Google Cloud, then adds AI-powered monitoring, automated compliance reporting, and a stack of dashboards that look impressive in board meetings. And then a breach happens anyway—triggered by something basic, like a misconfigured storage bucket that exposes sensitive data. That’s not a tooling gap. That’s a people, process, and governance gap. Misconfiguration remains a top driver of cloud risk because the cloud rewards speed, and speed without guardrails creates exposure. Identity has become the real perimeter, so compromised credentials and excessive privileges are more dangerous than many network threats. Shadow IT is still thriving, not because teams love breaking rules, but because governance often slows delivery to a point where groups route around controls. And automation doesn’t eliminate risk; it can scale mistakes and amplify noise when teams lack the skill and clarity to interpret findings and respond decisively. If you want a cloud security strategy that actually works, start with fundamentals: invest continuously in hands-on training that matches how fast cloud platforms change, establish clear accountability for configuration standards and exceptions, build cross-functional governance that enables the business to move quickly with guardrails, bring in outside experts for real knowledge transfer rather than checkbox audits, and treat every incident as fuel for continuous improvement instead of a one-off remediation. If your strategy is “buy another product,” you’re probably treating symptoms. If your strategy is “build competence, enforce guardrails, and create accountability,” you’re addressing the root problem. #CloudSecurity #Cybersecurity #CloudComputing #DevSecOps #IAM #SecurityGovernance #RiskManagement #CloudStrategy #MultiCloud #ZeroTrust What drives your cloud security strategy? https://lnkd.in/evYwKJuA