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Articles by Category: Tech Sketch

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
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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.
Transform Static AI Inventory Into a Risk Signal with Continuous AIBOMs
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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
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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.
Your Threat Model Is Already Out of Date
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Your Threat Model Is Already Out of Date

Traditional threat modeling breaks in SMEs because it assumes stable architecture, clear ownership, and spare security capacity. AI can reduce the cost of system understanding and first-pass analysis, but it cannot replace ownership, risk judgment, or governance.
Third-Party Cyber Risk Is Now an Uptime Problem
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Third-Party Cyber Risk Is Now an Uptime Problem

Third-party cyber risk is no longer a supplier-review problem. It is a service-survivability problem, and the dangerous vendor is often the one you cannot replace, work around, or operate without under pressure.
When Speed Meets Risk: Protecting API Keys in AI-Driven Development
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When Speed Meets Risk: Protecting API Keys in AI-Driven Development

AI has sped up software delivery, but it is also exposing API keys and other sensitive information. If this trend continues, businesses are basically doing half the job for bad actors and making it easier for exploitation to occur. CISOs and IT leaders must pair AI coding velocity with disciplined governance to keep their sensitive information secure.
The Emerging LLM Firewall Market: How to Evaluate Vendors
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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.
Paying for Premium But Getting Less: The Risk Behind AI Model Aggregators
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Paying for Premium But Getting Less: The Risk Behind AI Model Aggregators

AI model aggregators provide convenience and cost efficiency by providing multiple AI models for a single subscription. However, it is difficult for businesses to verify if they are using an advertised model or a substitute. CIOs and IT leaders must understand this risk and implement safeguards to verify models while using these services.
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.