🍪 Weekly Cookies 🍪
Quick, practical moves in cloud, security, and AI. Hey fam 👋🏿
Welcome to the very first edition of ByteWithMike Weekly, where we break down the real moves shaping cloud, security, and AI careers in 2026 💪🏿.
My promise: every week you’ll get sharp, actionable insights that you can actually use.
🧠 DevSecOps in 2026: The New Baseline
Security isn’t a phase anymore. It’s how modern software is built.
Teams that treat it like a final check keep getting breached.
The ones that win build it into every step — from the first commit to runtime.
Key Moves in 2026
• Automated scans run before code merges, using tools like Semgrep and Veracode.
• Secrets are locked down with GitGuardian, and misconfigurations are caught before deployment.
• IaC scanning tools like Checkov catch risky configurations before infrastructure is even provisioned.
☁️ Guardrails That Catch Risks Before They Exist
Focus: Building a safety net into every stage of delivery
Strong security doesn’t rely on people remembering to check every detail.
It uses guardrails that stop bad changes before they ever hit production.
What Smart Teams Are Doing
• Before deployment: Tools like OPA and Terraform Sentinel enforce policies automatically.
• During deployment: AWS SCPs and Azure Policy prevent non-compliant changes.
• After deployment: Continuous drift detection from Wiz or Prisma Cloud flags risky changes instantly.
🔥 Bonus Insight: How AI Is Transforming DevSecOps in 2026
The most forward-thinking teams aren’t just using AI for alerts.
They’re rebuilding how pipelines think, react, and defend.
1. Predictive Threat Modeling
Machine learning models simulate how attackers would move through your environment before code is deployed.
This surfaces dangerous patterns like over-privileged IAM roles or exposed S3 buckets before they ever reach production.
2. Intelligent CI/CD Enforcement
AI-driven policy checks automatically block risky commits, leaked keys, or vulnerable dependencies during builds — keeping security part of the flow instead of a late step.
Examples:
GitHub Advanced Security: https://docs.github.com/en/code-security
OpenAI GPT API: https://platform.openai.com/docs
3. Adaptive Incident Response
AI can now detect and respond to suspicious behavior without waiting for humans. It isolates compromised containers, revokes leaked credentials, or rolls back risky IaC automatically.
Examples:
AWS GuardDuty: https://docs.aws.amazon.com/guardduty
Microsoft Defender for Cloud: https://learn.microsoft.com/en-us/azure/defender-for-cloud
🤖 Threat Modeling for the LLM Era
This week’s focus: LLM-Aware Threat Modeling
• Who it’s for: Security engineers, architects, and anyone building AI-enabled systems.
• What it covers: Prompt injection paths, model abuse scenarios, and API misuse risks.
• Study plan: Combine OWASP’s LLM Top 10 with runtime validation tools like Guardrails AI or LangFuse.
AI is no longer a feature because it’s infrastructure. And that means new attack surfaces. Teams that model these risks early stay ahead of threats and ship faster with confidence.
🍪 Wrap-Up
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