The Software Efficiency Report | 2026 Week 25

Metric of the week

The Automation Threshold: 61%

Deep dive

Policy-as-Code: Scaling Compliance Without Slowing Delivery

Software Efficiency Metric of the Week

The Automation Threshold: 61%

Engineering teams must fully automate at least 61% of their software deployments from commit to production to be considered high-maturity.

The baseline is critical because jumping into AI code generation without established delivery automation creates huge pipeline bottlenecks. Many teams produce code faster than ever but struggle to ship it safely due to a reliance on manual verification and slow compliance auditing.

Key takeaway: Just as automated rollbacks have become a standard safety net for elite engineering groups, clearing the 61% deployment mark is the new barrier to entry for teams that want to scale modern software delivery without human intervention. Source Source

Reader Poll

What is your team’s policy for non-production cloud environments over the weekend?

My take: leaving test servers running all weekend is the engineering equivalent of leaving your car idling in the driveway for two days straight. With cloud waste jumping back up to 29% this year, it’s the easiest low-hanging fruit for any team to fix.

How proactive is your infrastructure governance right now?

What’s your approach?

A) Always-On: Everything stays running 24/7. Manual spin-downs take too long or risk breaking environment configurations.

B) The Reminder Method: We rely on developers to manually shut down their own sandboxes before logging off on Friday.

C) Scheduled Automation: We use automated tags and cron-jobs to kill all non-prod instances every Friday evening.

D) Ephemeral Environments: We don’t have permanent dev environments. Our platform spins them up on-demand via CI/CD and destroys them the moment a test finishes.

Engineering Tip of the Week

Stop executing your entire automated test suite on every single minor pull request. High-velocity engineering teams are shifting to Predictive Test Selection (PTS), using intelligence to run only the tests directly impacted by a specific code change.

Technology Ecosystem Trends

Top Ten developments shaping modern engineering operational efficiency this week and what they mean operationally.

1. Computer vision is transforming automated testing. Testing platforms can now validate user interfaces based on visual interpretation rather than fragile page structures, reducing maintenance effort and improving test reliability. Source
2. AI-assisted engineering is becoming measurable. New observability tools now allow organizations to track the productivity, quality, and operational impact of AI-generated code, giving leaders visibility into how AI contributes to software delivery outcomes. Further reading: Source Source Source
3. AWS and Google Cloud are deepening multi-cloud collaboration. A new partnership aims to simplify networking and identity management across both platforms, making multi-cloud architectures easier to operate and secure. Source
4. Software supply chain security is becoming a top DevSecOps priority. Organizations are investing heavily in Software Bills of Materials (SBOMs), build provenance verification, and artifact signing to strengthen trust in the software development process and reduce supply chain risks. Source
5. Continuous Quality Engineering is replacing traditional testing phases. Quality is increasingly becoming a continuous activity embedded throughout the delivery lifecycle, allowing teams to identify issues earlier through observability, analytics, and automated validation. Source
6. AI-powered DevOps is moving beyond assistance and into autonomous operations. Organizations are increasingly allowing AI agents to monitor systems, manage deployments, and respond to incidents with limited human intervention. This marks a shift in operational priorities from scripting automation to establishing governance, guardrails, and accountability for machine-driven decisions. Source
7. Progressive delivery is becoming the standard approach to releases. Modern deployment pipelines increasingly use live application metrics to determine whether a release should continue, pause, or roll back automatically, reducing the risk of production outages Source
8. MCP security risks are creating a new cybersecurity discipline. Growing concerns around Model Context Protocol vulnerabilities and agent-based attacks have led to specialized security training focused on protecting AI agents, integrations, and automation frameworks. Further reading: Source
9. Observability platforms are becoming more intelligent and automated. New AI capabilities can automatically connect production incidents to specific deployments or code changes, helping engineering teams identify root causes much faster. Further reading: Source
10. GPU Infrastructure Management Becomes the Next FinOps Challenge As AI adoption accelerates, organizations are realizing that GPU utilization is becoming as important as CPU utilization. Engineering teams are investing in GPU scheduling, workload optimization, capacity planning, and AI infrastructure cost governance. Source

Deep Dive Article:  Policy-as-Code: Scaling Compliance Without Slowing Delivery

One of the biggest mistakes I see in cloud transformation initiatives is treating compliance as a review activity rather than an engineering challenge.

I’ve seen this happen across healthcare, telecom, manufacturing, and startup environments. The regulations may be different, but the pattern is usually the same. Engineering teams move fast while compliance processes struggle to keep pace.

A platform team I worked with spent several weeks every quarter gathering audit evidence. What stood out was that the audit findings were rarely surprising.

The same issues kept appearing:

• Storage buckets without encryption
• Missing resource tags
• Excessive IAM permissions
• Network configurations that didn’t meet internal standards

Nobody was deliberately breaking the rules.

The real problem was that the rules lived in documents, spreadsheets, and review meetings. By the time someone checked whether standards had been followed, the infrastructure had already been deployed.

When those controls were built directly into the deployment pipeline, most of those recurring findings disappeared.

Not because engineers suddenly became security experts.

Because the process stopped depending on people remembering every requirement.

That’s where Policy-as-Code delivers its value.

Not by making compliance easier. By making compliance part of the system itself.

What Is Policy-as-Code?

Policy-as-Code (PaC) applies software engineering principles to governance, security, and compliance requirements.

Instead of documenting policies in PDFs and relying on manual reviews, organizations define policies as code and evaluate them automatically throughout the software delivery lifecycle.

Infrastructure-as-Code changed the way we provision infrastructure. Policy-as-Code changes the way we enforce standards.

Policies can be:

• Stored in Git
• Reviewed through pull requests
• Version controlled
• Tested before deployment
• Integrated directly into CI/CD pipelines

The benefit is straightforward. Teams get feedback while changes are being made, not weeks later during an audit or release review.

Turning a Policy into Code

Take a common security requirement:

All cloud storage buckets must be encrypted, and public access must be disabled.

Most organizations already have this requirement documented somewhere. The challenge is making sure it is consistently enforced.

Using Open Policy Agent (OPA), that requirement can become an automated control:

package cloud.storage

import rego.v1

default allow := false

allow if {

input.public_access == false

input.encryption_enabled == true

}

deny contains msg if {

input.public_access == true

msg := “Storage buckets cannot be publicly accessible.”

}

deny contains msg if {

input.encryption_enabled == false

msg := “Server-side encryption must be enabled.”

}

The logic is simple. Public access is not allowed, encryption must be enabled, and deployments that violate those requirements are blocked before reaching production.

If a Terraform configuration fails the policy check, the pipeline immediately reports the issue and explains what needs to be fixed. Developers get feedback while they’re still working on the change instead of discovering the problem later.

The good news is that teams don’t need to become Rego experts to get started.

Tools such as Checkov, Trivy, AWS Config, Azure Policy, and Google Cloud Organization Policies already include many built-in controls that organizations can adopt right away.

Where Teams See the Fastest Results

The storage bucket example is straightforward, but most organizations see the biggest impact when they automate controls that repeatedly create operational headaches.

Cost Management

One organization found that developers were regularly provisioning larger cloud instances than necessary. There was no malicious intent. Engineers were simply optimizing for speed and convenience.

A simple policy restricted deployments to approved instance families unless an exception was approved. Cloud costs dropped without adding another review process or approval workflow.

Kubernetes Security

Preventing containers from running as root is a common security requirement.

Without automation, security teams typically discover these issues during reviews or assessments. With tools like Kyverno or Gatekeeper, non-compliant deployments can be rejected automatically.

The conversation shifts from finding problems to preventing them.

Resource Tagging

Most enterprises require tags for ownership, environment, and cost allocation.

Without enforcement, tagging often turns into a quarterly cleanup project.

A policy can require mandatory tags before resources are deployed. Reporting improves, ownership becomes clear, and governance becomes much easier to maintain.

IAM Governance

Overly broad permissions continue to be one of the most common cloud security findings.

Policies can block deployments that contain wildcard permissions or excessive access rights. Instead of documenting risk after deployment, the risk is prevented before deployment happens.

Why Organizations Invest in Policy-as-Code

Faster Feedback

Issues are easier and cheaper to fix during development than after deployment.

Developers receive feedback while they still have full context about the change they are making.

Consistent Enforcement

Manual reviews naturally vary depending on who performs them, how much time they have, and how familiar they are with the environment.Policies apply the same standards every single time.

Better Audit Readiness

Every policy change becomes part of the normal software delivery process.

Organizations gain visibility into what changed, who approved it, when it changed, and why it changed. Audit evidence becomes a natural byproduct of day-to-day engineering work.

Governance at Scale

As cloud environments grow, manual governance becomes increasingly difficult to sustain.

Policy-as-Code enables platform and security teams to apply standards consistently across hundreds or even thousands of resources.

The Current Tooling Landscape

Several mature solutions support Policy-as-Code implementations today.

Open Policy Agent (OPA) : One of the most widely adopted frameworks for policy evaluation in cloud-native environments.

Gatekeeper and Kyverno : Popular options for enforcing policies within Kubernetes clusters.

Checkov : Focused on Infrastructure-as-Code scanning and compliance validation before deployment.

Trivy : Provides broader coverage across Infrastructure-as-Code, container security, vulnerabilities, secrets detection, and software supply chain security.

Cloud-Native Policy Services : Many organizations also rely on AWS Config, Azure Policy, and Google Cloud Organization Policies as part of their governance strategy.

Where Policy-as-Code Initiatives Struggle

Most Policy-as-Code projects don’t fail because of technology.

They struggle because of how they’re introduced.

Trying to Automate Everything at Once

I worked with a team that tried to codify dozens of controls before enforcing even one.

The result was hundreds of findings and very little confidence in the output. Developers stopped paying attention because the signal-to-noise ratio was too low.

Eventually, the team narrowed its focus to a small set of controls covering encryption, public access, and IAM permissions.

Adoption improved almost immediately.

Staying in Audit Mode Forever

This is one of the most common traps.

Teams deploy policies. Dashboards appear. Violations are reported. Everyone feels progress is being made.

Six months later, the same violations still exist because nothing is actually being enforced.

Audit mode is useful for building confidence.

Living in audit mode indefinitely defeats the purpose.

Ignoring False Positives

A poorly designed policy can be just as disruptive as having no policy at all.

I remember a policy update that blocked a legitimate deployment because a tagging rule didn’t account for a shared platform service.

The technology worked exactly as expected.

The policy didn’t.

That experience changed how we introduced new controls. Every policy started in audit mode and only moved to enforcement after we were confident the results were accurate.

Writing Policies Developers Can’t Understand

The best policies clearly explain three things:

• What failed
• Why it failed
• How to fix it

Developers shouldn’t have to decode security jargon to understand what action is required.

Getting Started

If you’re considering Policy-as-Code, avoid starting with a massive compliance framework.

Begin with a problem that repeatedly appears in audits, reviews, or incident investigations.

Good starting points include:

• Public storage access
• Missing resource tags
• Unencrypted resources
• Excessive IAM permissions

Run policies in audit mode first.

Measure false positives.

Refine the controls.

Build trust with engineering teams.

Then gradually move high-confidence policies into enforcement.

The most successful implementations rarely start with hundreds of policies.

They usually begin with a handful of controls that solve real problems.

The Bottom Line

Policy-as-Code isn’t primarily a security initiative. At its core, it’s a way to eliminate repetitive manual validation work. Every recurring audit finding, review checklist, or governance discussion should raise the same question:

Why is a person still validating something that a system could verify automatically?

The technology already exists. The harder part is deciding which controls matter and having the discipline to enforce them. Most organizations don’t have a Policy-as-Code problem.

They have an enforcement problem.

The policies exist. The dashboards exist.  The alerts exist.  But if a deployment can still violate a critical control and reach production, then the policy isn’t really a guardrail.

It’s just documentation.

If you’re unsure where your own delivery pipelines stand today, that’s exactly the type of visibility TuskerGauge is designed to provide.

Are your policies actively preventing risk, or are they simply measuring it after the fact?

Tools, Resources and Community | Worth Knowing

Open source Tools

Mender helps organizations manage and securely deliver over-the-air (OTA) software and operating system updates to embedded Linux and IoT devices. It enables remote device management, automated deployments, rollback capabilities, and fleet-wide update control without requiring physical access to devices. Source

Checkov is an open-source Infrastructure-as-Code (IaC) security and compliance scanning tool that identifies misconfigurations, security risks, and policy violations in Terraform, Kubernetes, CloudFormation, Helm, and other infrastructure definitions before deployment. Further reading: Source

Commercial Tool

New Relic is a full-stack observability platform that provides real-time monitoring of applications, infrastructure, logs, networks, and user experiences. It helps engineering teams quickly identify performance issues, troubleshoot incidents, and improve system reliability through AI-powered insights and analytics. Further reading: Source

Learning and Community

OpenTelemetry Community is a vendor-neutral observability community under the CNCF that develops standards for collecting, processing, and exporting telemetry data such as metrics, logs, and traces. It has become the industry’s preferred framework for building modern observability solutions. Further reading: https://opentelemetry.io/community

OWASP DevSecOps Guide provides practical guidance for integrating security throughout the software development lifecycle. It helps organizations adopt DevSecOps practices by embedding security controls, automated testing, threat modeling, and compliance checks directly into development and deployment workflows. Further reading: https://owasp.org

AWS Migration Hub is a centralized migration management service that helps organizations plan, track, and monitor application and infrastructure migrations to AWS. It provides visibility into migration progress, dependencies, and status across multiple AWS migration tools and services. Further reading: https://aws.amazon.com/migration-hub

Technology Ecosystem Weekly News Digest

Cloud and Platform Updates

• Microsoft temporarily expands GitHub infrastructure onto AWS to handle AI-driven demand. GitHub’s rapid growth in AI-generated code has increased platform load significantly, leading Microsoft to use AWS capacity alongside Azure while continuing its long-term migration strategy. Source
• Google Cloud continues rolling out platform updates across API and integration services. Recent Apigee updates focused on platform stability, operational improvements, and cloud API management enhancements. Reference: Source Source
• Multi-cloud strategies continue gaining traction among enterprise teams. Growing AI workloads, resilience requirements, and capacity planning challenges are driving organizations toward broader cloud diversification.Source Source

• AWS Summit discussions during the week focused on modernization programs, self-service platform engineering, cloud migration acceleration, and operational automation. AWS customers were also reminded of multiple database end-of-support milestones affecting Aurora, PostgreSQL, MySQL, and MariaDB deployments, prompting upgrade and migration planning activities. Source Source

Here is a portal to get other cloud news: Source

Open-Source and Linux Ecosystem

• Open-source security receives a major boost through Project Lightwell. IBM and Red Hat have announced a multi-billion-dollar initiative aimed at strengthening open-source ecosystems using AI-driven security analysis, dependency monitoring, and ecosystem-wide risk management. Source
• Software supply-chain security remains a primary concern, with industry research identifying dependencies, build systems, and developer tooling as the leading attack surfaces.: Source
• Community-driven software supply-chain security initiatives gained momentum, with broader adoption of SLSA, SBOM, artifact signing, and provenance verification frameworks. Reference: Source

• OpenSSF warned that 66% of open-source practitioners remain unprepared for the EU Cyber Resilience Act, raising concerns around compliance readiness and software supply-chain security. Source: Source (Source)
• Industry discussion intensified around securing open-source software used in AI systems, with calls for stronger funding and governance of critical open-source projects.: Source (Source)
• Microsoft continued expanding its Linux and open-source strategy around AI infrastructure, reinforcing the growing role of Linux, containers, and cloud-native tooling in enterprise AI platforms.: Source (Source
• The 2026 Open Source Security and Risk Analysis (OSSRA) report (published 3 months back) was released, highlighting continued growth in open-source adoption and increased focus on dependency governance and vulnerability management. Reference: Source

DevOps, Platform Engineering and SRE

• Open-Source CI/CD Abuse Detector: A tool utilizing LLMs to protect CI/CD pipelines from credential theft, as reported bySource.
• GitHub Enforces Self-Hosted Runner Updates: GitHub announced a new “brownout” schedule to force teams to update their self-hosted runners to safer versions, starting June 29, 2026, to ensure pipeline security. [1]
• GitHub is simplifying authentication for AI development workflows. The removal of personal access token requirements for agentic development reduces friction for autonomous coding systems while pushing organizations to modernize secrets management and identity controls.
• Trust3 AI Releases AgentDOS to Monitor Autonomous Agents: Trust3 AI launched a control plane called AgentDOS to watch over active AI agents within enterprise platforms, tracking data access and token use in real time. [1]
• CDEvents Standardizes Delivery: CDEvents has updated its standards to enable AI-powered DevOps platforms to receive structured data, notesSource.

Security and DevSecOps

• Microsoft released its largest Patch Tuesday ever, fixing more than 200 vulnerabilities, including multiple publicly disclosed and zero-day flaws. This was the dominant DevSecOps story of the week.: Source Source Source Source
• Security researchers noted that AI-assisted vulnerability discovery is contributing to larger Patch Tuesday releases, increasing pressure on vulnerability management programs.: Source
• GitHub announced npm v12 will disable install scripts by default, a major supply-chain security improvement designed to reduce malicious package attacks. Source: Source
• Enterprise security teams accelerated June patch deployment efforts, particularly for Windows HTTP.sys, SharePoint, graphics subsystem, and remote-code-execution vulnerabilities. Source: Source

Latest Security news: Source

AI/ML and Agentic AI

• Open-source security leaders warned that the rapid growth of AI models is outpacing security investments in the open-source ecosystem, creating new supply-chain risks. Source: Source
• Microsoft linked increased AI-assisted vulnerability discovery to the record-breaking June Patch Tuesday release, highlighting AI’s growing role in security operations. Sources: Source Source (Source)
• Google researchers published new details on TPU infrastructure evolution, describing advances in AI supercomputing scale, resilience, and efficiency. Source: Source

Three portals to get latest AI news : Source Source Source

Embedded Systems and IoT

• IoT Lifecycle & Software Strategy: A new industry report shows that software challenges remain the leading cause of product delays because engineering demands outpace existing infrastructure. Organizations are being urged to holistically overhaul device lifecycle management from product design to decommissioning to avoid bottlenecks. Learn more on the Source
• DevSecOps & Security Integration : With less than 100 days until the EU Cyber Resilience Act enforcement, IoT manufacturers face massive pressure to secure software inventories. Experts warn that widespread gaps in Software Bill of Materials (SBOM) visibility are threatening compliance readiness.Source.
• Continuous Testing & Flash Simulation: Developers can now test real-time IoT databases without physical target hardware thanks to newly integrated NVMe flash simulation tools. This eliminates the need for expensive evaluation boards and shortens continuous testing cycles.  [1]
• Open Source Tools Integration: The Zephyr Project community highlighted how modern open-source RTOS architectures allow developers to build cross-platform code without sacrificing system security. This open ecosystem dramatically reduces the time engineering teams waste rewriting driver layers.[1]
• Software supply-chain security remained a major focus for embedded and IoT software vendors, driven by Cyber Resilience Act preparation and SBOM requirements. Source: Source
• Open-source security discussions increasingly emphasized protection of foundational software components used in embedded and edge systems.: Source
• Cloud-native edge and Kubernetes-based infrastructure continued gaining attention ahead of KubeCon India, particularly for distributed and edge deployment architectures.: Source