📰 Dev.to · Bala Paranj

Most cloud security tools infer intent from configuration. This is guessing. There's a different model: declare intent, then deterministically check config agai

Most cloud security tools check settings one at a time. The breaches come from how settings combine. The maintainers of the best open-source scanner have been s

Organizations are drowning in 'Critical' CVEs in container images, most of which are never exploitable. The scanner found a vulnerable library. The application

We spend millions scanning storage to 'discover' where sensitive data ended up. The question nobody asks: why didn't the system know what kind of data it was ho

Zero Trust says 'only allow required network flows.' Nobody declares which flows are required. So the industry compares what's allowed against what's observed a

Every framework mandates least privilege. Every organization fails at it. Because the principle assumes an artifact that doesn't exist: a machine-readable decla

Meta's config change safety architecture — canary deployments, progressive rollouts, health checks that catch regressions across billions of users is the most b

Meta's Capacity Efficiency architecture advances past the LangChain/OpenAI harness baseline in three specific ways: tools/skills separation, offense/defense uni

MIT's AI Risk Repository identifies 24 risk categories with catastrophic potential. The engineering teams building AI systems aren't reading it, and the study d

We keep reaching for probabilistic tools on problems that have a definite answer, and deterministic tools on problems that don't. Snowden's framework names the

Anthropic's security team published seven recommendations for the AI era. Every one says 'use AI to react faster.' Not one says 'declare what must be true and v

Jen Easterly correctly identified that cybersecurity is an aftermarket for software quality failures. Then she celebrated an AI that makes the aftermarket faste

A completeness law — independently confirmed by engineering, cybernetics, and economics — names the five elements any viable system must contain. The current AI

We are uncovering better ways of building software with AI agents. Through this work we have come to value certain things over others.

Cloud has engines (Lambda, Kubernetes), transmission (CI/CD, IaC), and tools (APIs, admission controllers). What it's missing is the control unit — the part tha

Vivek Trivedy's 'Anatomy of an Agent Harness' is the clearest statement of how the industry thinks about AI agents. A NIST mathematical proof just demonstrated

CSPM, CNAPP, SIEM, SOAR, IaC scanners, ML detection — we applied TRIZ Function-Failure Analysis to each. The missing function is the same in all six. It's also

Resource tagging is the engineering bridge between human intent and mathematical verification. A tag turns subjective purpose into a state variable that automat

Some cloud security problems are structural, not behavioral. For those, a few lines of declarative constraints outperform thousands of lines of scanner code.

Nuclear power, medicine, law, aerospace, military operations, and aviation each faced the same contradiction software is struggling with now. They solved it dec

Adding agents without specifications is like adding nodes without protocols. Distributed systems learned this 40 years ago. The coordination mechanisms are the

Guardrails, LLM-as-judge, AI code review — they all share one structural problem. The verifier has the same failure modes as the thing it's verifying. Here's wh

APIs got a standard format and an ecosystem followed. Properties, contracts, and rationale have no standard. AI generates code at scale without consuming the sp

David Parnas proved that human cognitive capacity is the hard bottleneck of software engineering. 53 years later, AI coding agents are proving him right — and h