Beyond Automation – Part 5: Cybersecurity Without Analysts: The Attack Surface Created by AI Defenders

Feb 16, 2026

Series context. This article is Part 5 of Beyond Automation: Why Human Judgment Remains Critical in AI Systems. The series examines how removing or weakening human oversight in high-stakes domains creates new, often invisible, failure modes. This installment focuses on cybersecurity, where autonomous detection and response systems increasingly operate at machine speed while adversaries adapt just as quickly. [1]

Automation Promised Speed. It Also Created New Exposure.

Security teams adopted AI to solve a real problem: scale.

Modern environments generate more alerts, telemetry, and attack signals than human analysts can process. Autonomous SOC tooling promised to detect, decide, and respond faster than attackers could move.

What it also introduced was a new attack surface. [2]

When Evidence Systems Break: Lessons from Independent Police Evidence Audits – Part 2

Feb 12, 2026

Series context. This article continues When Evidence Systems Break: Lessons from Independent Police Evidence Audits. Part 1 established that evidence failures are operational risk events driven by system drift rather than misconduct. Part 2 examines what independent reviewers consistently observe during evidence audits and why internal reviews, despite good intentions, often fail to surface cumulative risk early.

Independent Audits Look at Systems, Not Incidents

Internal evidence reviews are typically incident-driven. They focus on whether a specific discrepancy can be explained, corrected, or documented away.

Independent evidence audits start from a different premise. They assess whether the system as a whole can reliably produce defensible evidence outcomes under routine conditions, stress, and scrutiny. The question is not whether today’s evidence can be justified, but whether tomorrow’s evidence will withstand challenge. [1][2]

Forensics and Futures: Navigating Digital Evidence, AI, and Risk in 2026 – Part 3

Feb 9, 2026

Series context. Part 3 of the Forensics and Futures series examines how large language models (LLMs) are being integrated into forensic and investigative workflows. Following Part 2’s focus on forensic readiness and defensibility, this installment addresses where LLMs add value, where they introduce new risk, and how organizations should govern their use in 2026. [1]

Why LLMs Are Fundamentally Different From Traditional Forensic Tools

Large language models are being adopted in investigative environments for one primary reason: scale. Modern investigations routinely involve millions of messages, emails, documents, and transcripts. LLMs enable navigation of that volume within timeframes that human-only review cannot sustain.

Common capabilities include summarizing large text collections, clustering conversations, identifying recurring themes, and surfacing entities across datasets. These functions make LLMs attractive for reviewing chat logs, collaboration platforms, email corpora, document repositories, and transcription outputs.

The distinction that matters is this: LLMs do not extract evidence. They interpret content. Traditional forensic tools acquire artifacts and preserve them in a verifiable state. LLMs operate one layer above that process, generating linguistic interpretations rather than discovering new data.