Practical Security Analysis from the Field
Deep examinations of industry incidents, vendor risk, and operational security decisions – no certifications required, just 25+ years of experience.
Identity is the real perimeter in modern environments. As service accounts, API keys, and federated access sprawl across SaaS, cloud, and APIs, organizations lose visibility, control, and the ability to enforce least privilege—turning identity debt into one of the most dangerous and persistent cyber risks.
Most cybersecurity vendors now claim “AI integration,” but few can explain what their AI actually does or how it makes operational decisions. While chat-based AI tools like Microsoft Copilot excel at individual productivity tasks, they introduce dangerous variability when applied to operational security work that requires consistency, auditability, and institutional knowledge.
This analysis examines why conversational AI fails in SOC analysis, GRC assessments, and compliance work—where a single word in a prompt can trigger vastly different risk classifications and operational outcomes. The core issue isn’t the technology itself, but the structural mismatch between tools designed for exploratory work and processes that demand repeatable, auditable results.
Drawing from real-world implementation experience, this piece explores the hidden risks of context pollution, judgment variance, and governance gaps in AI-powered security operations. It presents a practical alternative: modeling AI as stateless services that encode institutional expertise while eliminating the variability that makes chat-based approaches unsuitable for regulated environments. Essential reading for security leaders navigating AI adoption without compromising operational integrity.
Most security teams believe they have better visibility than they actually do. In modern SaaS and cloud environments, logging gaps are structural, costly, and often invisible until an incident exposes them.
Perfect security is impossible. Learn how to manage risk proportionally, avoid burnout, and build sustainable security programs that improve incrementally over time.
You can’t secure what you don’t understand. Before threat hunting, tooling, or remediation, security teams must confront the messy reality of undocumented systems, identity sprawl, data drift, and technical debt. This piece explains why environment discovery is foundational security work—and why skipping it undermines everything that follows.
Certifications and frameworks don’t prepare you for how security actually works inside real organizations. This series focuses on the judgment, trade-offs, and organizational realities that define effective security work.
Identity is the real perimeter in modern environments. As service accounts, API keys, and federated access sprawl across SaaS, cloud, and APIs, organizations lose visibility, control, and the ability to enforce least privilege—turning identity debt into one of the most dangerous and persistent cyber risks.
Most cybersecurity vendors now claim “AI integration,” but few can explain what their AI actually does or how it makes operational decisions. While chat-based AI tools like Microsoft Copilot excel at individual productivity tasks, they introduce dangerous variability when applied to operational security work that requires consistency, auditability, and institutional knowledge.
This analysis examines why conversational AI fails in SOC analysis, GRC assessments, and compliance work—where a single word in a prompt can trigger vastly different risk classifications and operational outcomes. The core issue isn’t the technology itself, but the structural mismatch between tools designed for exploratory work and processes that demand repeatable, auditable results.
Drawing from real-world implementation experience, this piece explores the hidden risks of context pollution, judgment variance, and governance gaps in AI-powered security operations. It presents a practical alternative: modeling AI as stateless services that encode institutional expertise while eliminating the variability that makes chat-based approaches unsuitable for regulated environments. Essential reading for security leaders navigating AI adoption without compromising operational integrity.
Most security teams believe they have better visibility than they actually do. In modern SaaS and cloud environments, logging gaps are structural, costly, and often invisible until an incident exposes them.
Perfect security is impossible. Learn how to manage risk proportionally, avoid burnout, and build sustainable security programs that improve incrementally over time.
You can’t secure what you don’t understand. Before threat hunting, tooling, or remediation, security teams must confront the messy reality of undocumented systems, identity sprawl, data drift, and technical debt. This piece explains why environment discovery is foundational security work—and why skipping it undermines everything that follows.
Certifications and frameworks don’t prepare you for how security actually works inside real organizations. This series focuses on the judgment, trade-offs, and organizational realities that define effective security work.
Identity is the real perimeter in modern environments. As service accounts, API keys, and federated access sprawl across SaaS, cloud, and APIs, organizations lose visibility, control, and the ability to enforce least privilege—turning identity debt into one of the most dangerous and persistent cyber risks.
Most cybersecurity vendors now claim “AI integration,” but few can explain what their AI actually does or how it makes operational decisions. While chat-based AI tools like Microsoft Copilot excel at individual productivity tasks, they introduce dangerous variability when applied to operational security work that requires consistency, auditability, and institutional knowledge.
This analysis examines why conversational AI fails in SOC analysis, GRC assessments, and compliance work—where a single word in a prompt can trigger vastly different risk classifications and operational outcomes. The core issue isn’t the technology itself, but the structural mismatch between tools designed for exploratory work and processes that demand repeatable, auditable results.
Drawing from real-world implementation experience, this piece explores the hidden risks of context pollution, judgment variance, and governance gaps in AI-powered security operations. It presents a practical alternative: modeling AI as stateless services that encode institutional expertise while eliminating the variability that makes chat-based approaches unsuitable for regulated environments. Essential reading for security leaders navigating AI adoption without compromising operational integrity.
Most security teams believe they have better visibility than they actually do. In modern SaaS and cloud environments, logging gaps are structural, costly, and often invisible until an incident exposes them.
Perfect security is impossible. Learn how to manage risk proportionally, avoid burnout, and build sustainable security programs that improve incrementally over time.
You can’t secure what you don’t understand. Before threat hunting, tooling, or remediation, security teams must confront the messy reality of undocumented systems, identity sprawl, data drift, and technical debt. This piece explains why environment discovery is foundational security work—and why skipping it undermines everything that follows.
Certifications and frameworks don’t prepare you for how security actually works inside real organizations. This series focuses on the judgment, trade-offs, and organizational realities that define effective security work.