AI with Integrity: Part 4 — Shadow Algorithms: The Hidden Risks of Unvetted AI in Corporate IT

Oct 24, 2025

Series context. This installment extends Part 1 on AI as evidence and Part 2 on governance, then follows Part 3 on chain of custody, to tackle a growing reality, AI features and tools slip into enterprise workflows before security, legal, or audit can evaluate them. We show how to surface shadow AI, control it without crushing speed, and anchor decisions to recognized standards so they stand up under scrutiny. [1][2][3] (lcgdiscovery.com)

Why shadow algorithms are a growing risk

AI capabilities are now integrated into email clients, office suites, browsers, marketing platforms, developer tools, and SaaS add-ins. Well-meaning teams implement these features because they quickly address problems; however, the same convenience can result in sensitive data being sent to third-party models, changing evidence provenance, or producing outputs that appear authoritative without supporting test results. This is no longer just a theoretical governance issue. NIST’s AI Risk Management Framework considers AI risks as enterprise risks, not just science-project risks. It provides practical steps around Governance, Map, Measure, and Manage so that business owners and assurance teams speak the same language. [2]

After Utah, Part 5: Contracts, Insurance, and Governance Before the Event

Oct 22, 2025

Series context. This series turns lessons from the September 10, 2025, Utah shooting into a practical playbook for campuses, event organizers, and public officials. Parts 1–4 covered threat assessment, drone exposure, and venue operations. This installment closes the loop with the governance, contracts, and insurance moves that decide outcomes before doors open. [1]

Put risk management into the contract, not a binder on a shelf

When events are public, outdoors, and high profile, operational discipline is only as strong as the paperwork that gives people authority, budgets, and stop-show power. Event agreements, vendor scopes, and permits should hard-wire incident command roles, training, pre-event exercises, and escalation thresholds that align with national guidance for mass gatherings and incident management. That means planning checklists and role definitions tied to CISA’s mass gathering framework, FEMA’s National Incident Management System, and the ASHER program practices reflected in NFPA 3000. [2][3][4] (CISA)

LCG perspective. You cannot transfer accountability; you can only transfer portions of financial exposure. Write the plan into the contract, name the person with authority, and make payment milestones depend on delivering and exercising the plan.

Beyond the Screen: The Next Frontier of Digital Forensics – Part 4

Oct 20, 2025

Series context. This fourth installment continues our exploration of emerging evidentiary frontiers. Earlier parts examined video manipulation, mobile payments, and digital evidence chains. Now we turn to audio, where AI-generated voices and synthetic recordings are forcing courts to revisit assumptions about what “authentic” means. [1]

A Crisis of Trust in Recorded Voices

For decades, recordings carried an air of truth in court. A confession on tape, a 911 call, or a heated negotiation was seen as nearly self-proving, however, like in other areas we have discussed, times have changed. Generative AI now enables anyone to clone a voice from just seconds of sample audio, producing lifelike speech that is indistinguishable to the naked ear from genuine recordings. Fraudsters have already leveraged this to authorize wire transfers, fabricate threats, and impersonate public officials [2].

Courts are faced with a dilemma and previously when a witness could corroborate a recording based upon first-hand knowledge of the person who was recorded, authentication of modern audio evidence demands more than a casual assertion that “it sounds like him.” Courts and regulators are signaling that voice recordings must be validated through scientifically reliable methods, not mere perception.