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Articles by Tag: AI Governance

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.
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
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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.
The Rise of LLM Firewalls: Securing the New AI Attack Surface
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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.
From Autonomy to Accountability: Managing Agentic AI Risks
Strategy Brief

From Autonomy to Accountability: Managing Agentic AI Risks

Agentic AI shifts automation from single-task models to autonomous decision-makers, amplifying risks of misalignment, bias, and data leakage. OWASP’s new guidance equips SMEs with lifecycle security practices, ensuring governance, transparency, and resilience as autonomous agents move from experimentation into production. IT leaders and CISOs should read this article to learn how to secure agentic AI in production using OWASP’s guidance.
Autonomous Prescription Renewals: Innovation, Oversight, and the Liability Bill
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Autonomous Prescription Renewals: Innovation, Oversight, and the Liability Bill

Utah has authorized an autonomous AI system (Doctronic) to renew certain non-controlled prescriptions. The real story isn’t that AI can click refill, it’s that a state has started testing delegated clinical authority via a legal instrument–a regulatory mitigation agreement that partially sidesteps traditional only-licensed-humans-prescribe assumptions.
Learning from Shadow AI: Delivering the AI Tools Your Employees Actually Need
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Learning from Shadow AI: Delivering the AI Tools Your Employees Actually Need

As AI adoption surges, shadow AI was bound to follow, just like shadow IT before it. This can lead to data leaks and compliance violations, prompting urgent alarms when detected. However, it is also important to understand why shadow AI occurs. By uncovering its root causes, CISOs and IT leaders can close gaps and deploy the AI tools that employees truly need.