We use cookies to personalize content and to analyze our traffic. Please decide if you are willing to accept cookies from our website.
Advanced Settings
Become A Client!

Articles by Tag: AI Governance

The New Cost of AI Code Nobody Owns
Tech Sketch

The New Cost of AI Code Nobody Owns

AI-assisted coding is more than a developer-productivity issue, it is a production-accountability issue. This makes the executive decision clear. Permit AI-assisted development broadly, but block material production changes unless a named human can explain, support, secure, and reverse the change.
Your AI Bill Is Late Evidence
Tech Sketch

Your AI Bill Is Late Evidence

Agentic AI cost control is moving past budget caps, usage dashboards, and generic FinOps reporting. The harder problem is that spend is generated inside the dynamic execution paths of context expansion, retrieval, tool calls, retries, verification loops, model routing, and human rework.
When AI Becomes a Metered Service, CIOs Need More Than a Budget Cap
Strategy Brief

When AI Becomes a Metered Service, CIOs Need More Than a Budget Cap

A budget cap can stop a bill from crossing a threshold. However, it cannot tell a CIO which workloads should use premium models, which prompts are wasteful, when caching matters, whether long context is necessary, or which business unit is consuming AI because usage is easy rather than because it improves an operating result.
AI Coding Gains Are Real. The Hidden Cost Is Moving Downstream
Strategy Brief

AI Coding Gains Are Real. The Hidden Cost Is Moving Downstream

AI coding tools can accelerate development, but the hidden cost often moves downstream into review, validation, release, and remediation. CIOs should scale selectively, fund the control layer, and measure whether the whole delivery system improves. Not just whether developers generate code faster.
Transform Static AI Inventory Into a Risk Signal with Continuous AIBOMs
Tech Sketch

Transform Static AI Inventory Into a Risk Signal with Continuous AIBOMs

AI governance is becoming an evidence problem. CIOs need to prove that production AI systems still match the models, data, prompts, suppliers, and controls originally approved. Continuous AI Bills of Materials turn static inventory into a risk signal, helping leaders detect material change, route accountability, and avoid premature governance tooling.
Today’s Best AI Model Becomes Tomorrow’s Operating Risk
Tech Sketch

Today’s Best AI Model Becomes Tomorrow’s Operating Risk

AI models are becoming managed-platform dependencies with retirement dates, behavioral drift, and vendor-controlled lifecycles. CIOs should treat model replaceability as an operational resilience control before production AI becomes tomorrow’s fragile legacy.
AI Token Sprawl: Govern Developer Agents by Workflow Value, Not Consumption
Strategy Brief

AI Token Sprawl: Govern Developer Agents by Workflow Value, Not Consumption

As AI coding tools and agentic workflows become embedded in software delivery, CIOs need to govern AI spend by business value, workflow impact, and platform dependency. Not by seats, prompts, requests, or tokens alone.
EAI Reliability: Why Quiet Failures Need Runtime Supervision, Not Better Dashboards
Strategy Brief

EAI Reliability: Why Quiet Failures Need Runtime Supervision, Not Better Dashboards

AI systems can remain available and appear healthy while gradually becoming wrong, brittle, or misaligned. For the C-suite, this shifts the question of EAI’s reliability from a narrow engineering concern to a governance, assurance, and operating-model issue.
The Emerging LLM Firewall Market: How to Evaluate Vendors
Tech Sketch

The Emerging LLM Firewall Market: How to Evaluate Vendors

LLM risks are real, but not every deployment needs a firewall. Premature adoption adds cost without reducing exposure. The decision hinges on user trust, data sensitivity, and model autonomy. This guide helps CIOs and CISOs decide when to deploy, how to tier risk, and what to evaluate before committing to a vendor.
The Rise of LLM Firewalls: Securing the New AI Attack Surface
Tech Sketch

The Rise of LLM Firewalls: Securing the New AI Attack Surface

Large language models introduce behavioral security risks that traditional defenses were not designed to address. Research highlights persistent vulnerabilities such as prompt injection, RAG poisoning, and agent exploitation. LLM firewalls are emerging as a policy enforcement layer that inspects prompts, responses, and tool interactions to reduce exposure. CIOs, CISOs, and CTOs should assess where LLM deployments create new security risks and determine whether LLM firewalls are warranted in their environments.