Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Most AI deployment policies stop at approved chat interfaces. Meanwhile, employees install browser copilots, AI extensions, and third-party plugins that never touch Microsoft's management stack. IT can't configure what it can't see, and Group Policy and Intune only govern Microsoft's world. This post covers what actually happens once AI tools show up outside policy, five things most teams miss, and how PolicyPak enforces controls directly on the apps and browser extensions themselves.

“A man’s worth is no greater than the worth of his ambitions.” Marcus Aurelius wrote that almost two thousand years ago, in a private journal he never intended anyone to read. Coming from a man who held every title Rome could give, it’s a telling way to measure worth: not by what you hold, but by what you aim at. It has stuck with me, because everything I’ve seen in my career backs it up. Deep technical knowledge is where great engineering starts.

Say a sales rep uses an AI assistant to help manage their pipeline. The rep has role-based access to Salesforce, scoped to their territory and their accounts. The assistant, wired in through an API integration, often doesn't have that same scoping. It authenticates as a service account with broad read and write access across the org, because that was faster to set up than a permission model that matches what the actual user is allowed to see.

Unknown block type "undefined", specify a component for it in the `components.types` option AI is creating urgency at the board level and a definitional problem in the market.

Most teams conflate two questions that require separate tools: who can access a sensitive share right now, and who changed access or opened files. Native Windows utilities resolve neither at scale, so audits stall and incidents lose their trail. The tool you need calculates effective access across nested groups and captures every permission change with attribution across Windows, network-attached storage, and SharePoint.

Access review tools differ more than their marketing suggests: standalone certification platforms, full identity governance suites, and compliance automation tools all call themselves access review solutions and solve different problems at different costs and with varying implementation overhead. Matching governance maturity and compliance framework to the right category matters more than comparing feature lists, especially when audit evidence must prove that rejected access was actually removed.

AI hacking tools are compressing the time between finding a target and logging in as them. Password spraying, already responsible for the vast majority of identity attacks, is the technique benefiting most. Attackers use AI hacking methods to optimize timing, rotate infrastructure, and personalize lures at a scale no human operator could match manually.

Unknown block type "undefined", specify a component for it in the `components.types` option Every conversation about AI and early-career employees seems to start in the same place: will it weaken foundational skills by doing too much of the work? That's a reasonable concern. It's also not the question that ended up mattering most this summer.

A self-hosted password vault runs on infrastructure you control instead of a vendor's cloud, giving you direct custody of encryption keys, backups, and access logs. It trades vendor convenience for operational responsibility: you patch it, you back it up, and you decide who reaches it. For teams with data residency requirements, air-gapped environments, or a board that keeps asking where the credentials live, that trade is usually worth making.

I ran an audit against every MS-ADTS LDAP extended control. Most behave exactly as documented; two stood out for potential offensive use. Both abusing legitimate controls, but neither a privilege escalation: The unifying theme: a documented LDAP control, used as intended at the mechanism level, produces an effect Microsoft's telemetry and most defenders don't expect. Demonstrated against a two-DC cloud.lab (Windows Server 2022, forest functional level 2016). Lab / authorized-research context only.