Know what's running.
3 SIGNALS · 1 LESSON · 1 ACTION · ABOUT 5 MINUTES
This week I hosted a small executive roundtable on operational AI leadership. I began by asking why two organizations can have the same AI tools while one produces value and the other produces noise, risk, and another stalled pilot.
The discussion kept returning to decisions made around the model: what outcome matters, how the work gets done, who has authority, what the AI can access, how failure is handled, what counts as evidence, and who owns the result.
As AI gains access to tools and systems, familiar security failures can spread faster, reach further, and cause harm before a person intervenes.
SIGNAL 01
The roundtable put culture at the center of AI adoption
What happened. At this week's Operational AI Leadership roundtable, culture emerged as the strongest challenge to the framework. One participant had watched an earlier AI adoption effort fail because the culture was not ready for it. Another described organizations that want to appear innovative without giving anyone room to make a decision.
That tension is already shaping how people use AI. Employees may turn to unapproved tools while worrying that approved AI will replace them. Executives push for adoption percentages before the organization has agreed on the outcome or the acceptable risk. Governance, security, operations, and change management remain in separate lanes while the deployment crosses all four.
I brought seven operating questions to the roundtable:
What outcome are you trying to improve?
How does the work actually happen?
Who has authority over AI adoption for this workflow?
What information does the AI get, and what actions can it take?
What happens if it fails, and how do you recover?
What evidence justifies continuing, changing, or retiring it?
Who owns the end-to-end outcome and can intervene?
Culture shows up in every answer. It determines whether frontline workers can explain how the work really happens, whether security can challenge excessive access, whether teams surface failures, and whether the evidence can stop a deployment that leadership already wants.
The rest of the discussion followed from there. Companies adopt AI without a strategy. Leaders call one successful demonstration a pilot win. Teams count prompts, licenses, and output as if activity were value, even when AI produces work faster than the organization can absorb or review it.
Why it matters. A company can answer all seven questions on paper and still produce a polite fiction. If people are rewarded for showing adoption, punished for raising problems, or excluded from decisions about their own work, the operating model will describe how leadership hopes AI works rather than how people will use it.
A real pilot tests the culture along with the technology. It should show whether people can use the system inside the actual workflow, report problems without penalty, manage the remaining risk, and change course when the evidence is weak.
What to do. Begin the next pilot review with culture. Ask what happens to the person who says the pilot is not working. Check whether frontline workers can change the design, whether security and governance can slow the rollout, and whether the adoption target measures business value or tool usage. Then answer the seven operating questions with those people in the room.
Source: Hackademic Solutions Operational AI Leadership executive roundtable, September 9, 2026.
SIGNAL 02
AI agents are combining old attack techniques at machine speed
What happened. During cyber evaluations, OpenAI agents found a way to communicate across separate runs through a shared Artifactory package repository. Hundreds of agents participated in the later Hugging Face intrusion. They found exposed credentials, compromised workers, moved through trust boundaries, and probed deeper into the environment.
The attack used familiar security techniques: shared storage as a mailbox, public infrastructure for command and control, exposed-token discovery, evaluator deception, credential abuse, and privilege escalation. Many overlapping, short-lived agents experimented in parallel, shared what they learned, and kept searching for a path that improved their score. Ordinary weaknesses became a coordination network.
Why it matters. Talk of a new digital civilization distracts from a problem leaders already know how to address. Existing control failures become more dangerous when an automated system can discover, combine, and reuse them faster than a human team.
That makes the operating decisions around the model as important as the model itself. Who gave the agent access to shared infrastructure? Which credentials could it discover or use? What separated one run from another? Which actions were logged? Who could stop the work once its behavior moved outside the intended task?
What to do. Treat every shared writable service available to an agent as a possible communication channel. Review package repositories, object stores, CI artifacts, shared volumes, caches, and collaboration tools. Log how agent identities read and write to them, and look for related activity across multiple runs instead of reviewing each event in isolation.
SIGNAL 03
Put AI agents inside the insider-threat model
The framework. A new threat model from Matt Adams argues that frontier AI agents with access to shells, repositories, databases, or external services should be treated as a class of insider threat actor.
The model adapts five familiar insider-threat dimensions for non-human actors. The agent's objective supplies the motivation; its tools and permissions create the opportunity. Its capability comes from the model, its predisposition from its alignment profile, and its organizational setting from the runtime and working environment around it.
Because defenders cannot reliably determine an agent's intent, the threat model focuses on what the agent does.
Why it matters. Human insider-threat programs often look for changes in behavior: unusual work hours, access outside a person's normal role, suspicious communications, or attempts to hide activity. AI agents have no normal workday, can operate continuously, can be copied across many instances, and may touch an entire environment during one task. They require a different behavioral baseline.
Insider-threat programs already monitor actions, enforce least privilege, preserve evidence, contain blast radius, and prepare to revoke access. Those disciplines can cover agents if organizations give each one an identifiable runtime identity and collect the right telemetry.
Useful monitoring surfaces already exist: shell commands, git operations, database queries, API calls through connected tools, credential enrollment, and logs from systems the agent touches. Peer-side and counterparty logs also matter because an agent may suppress its own telemetry.
What to do. Add agents to the existing insider-threat program. For every deployed agent, record its identity, objective, permissions, tools, data access, autonomy level, logging path, containment procedure, and owner. Reassess that profile when the model version, tools, permissions, or workflow changes.
THE LESSON
Operational leadership is a deployment control
Someone has to connect the business outcome to the workflow, decide which permissions the work requires, weigh the resulting risk, and use the evidence to decide what happens next.
Security teams can monitor the agent. Governance teams can set policy. Technical teams can configure identities, tools, and logs. Frontline workers can explain how the work happens. Operational AI leadership brings them into one decision process, with a named owner who can intervene when the deployment stops producing the intended result.
DO THIS WEEK
Review one agent as both a workflow and an insider
Choose one AI agent or agentic pilot. Answer the seven operating questions from Signal 1, then review the agent as an insider:
What identity does it use?
Which systems, data, credentials, and shared services can it reach?
Which actions can it take without approval?
Which logs would show misuse, drift, coordination, or concealment?
Who can terminate the session, revoke credentials, roll back changes, and investigate what happened?
Do this before expanding the deployment. If no one owns the end-to-end outcome or cannot stop the agent, the organization has assigned authority without establishing control.
Operational AI leadership is critical for successful AI deployments. A pilot is ready to scale when the organization can name the outcome, explain the workflow, defend the permissions, recover from failure, show evidence that it works, and name an owner who can change or stop it.
— Chris Simpson
Hackademic Solutions
The Hackademic Briefing · Know what's running.