Know what's running.
3 SIGNALS · 1 LESSON · 1 ACTION · ABOUT 5 MINUTES
Last issue was about the AI you can't see. This issue is about designing the AI you can: who mandates it, where its controls actually live, and how to read one decision three ways.
SIGNAL 01
MIT is teaching your executives to deploy AI agents
What happened. I recently completed MIT’s Applied Agentic AI for Organizational Transformation course with leaders learning how to move autonomous agents from pilot to production.
My biggest takeaway had little to do with the model itself. The real work is understanding how the organization actually operates, where a process is broken or expensive enough to improve, and where an agent can safely help. The right starting point is not “where can we add AI?” It is “which process deserves automation, and what could go wrong if the automation acts on its own?”
Why it matters. Agent adoption is likely to arrive from the top, not from IT. Executives are being trained to transform with agents. Far fewer are being trained to govern and secure them.
That gap matters because agents do not just generate text. They can touch enterprise systems, retrieve sensitive data, trigger workflows, send messages, update records, and make decisions at machine speed. The unresolved questions are not academic: What permissions should the agent have? Who is accountable when it acts wrongly? How will anyone know what it did? When should a human stay in the loop? How do you shut it off?
This is the shadow-AI visibility problem from Issue #1, now with a syllabus and a mandate.
What to do. Get security into the agent-strategy conversation before the first pilot.
Require every proposed agent use case to declare six things up front: permissions, data access, system actions, human checkpoints, monitoring, and shutoff criteria.
If the team cannot explain what the agent can access, what it can do, who reviews its actions, and how it can be stopped, it is not ready for production.
Source: MIT Sloan
SIGNAL 0o2
A major AI vendor just made the harness a product category
What happened. Databricks released Omnigent as an open-source meta-harness and introduced a managed beta version on its platform. Rather than replacing AI agents, Omnigent sits above them, providing shared controls for permissions, security policies, persistent sessions, collaboration, and cost management.
Some of those controls are surprisingly specific. An agent can be granted permission to modify only the documents it created rather than every document it can access. Administrators can define spending limits that automatically pause an agent when it exceeds a budget. The individual features matter, but the bigger story is that a major AI platform now treats the harness itself as a product.
Why it matters. Most AI discussions still revolve around models: which one reasons better, writes better code, or tops the latest benchmark. Production systems, however, succeed or fail based on everything wrapped around the model.
The harness is the operational layer that provides context, grants permissions, limits authority, enforces policy, monitors behavior, logs activity, and contains failures. It is where AI strategy, cybersecurity, and governance become an operating system instead of three separate documents. It is also where much of the real security risk lives.
I explored this in my recent LinkedIn article, “The AI Harness: Your Best Control and Your Newest Attack Surface.” Using the recent Cursor vulnerabilities as an example, the lesson was straightforward: prompt injection was the initial entry point, but weaknesses in the surrounding control layer allowed the attack to escalate. The model was fooled, but the harness determined the consequences.
Databricks’ announcement reinforces that this architectural layer is becoming a first-class part of enterprise AI. Vendors are beginning to compete not just on models and agents, but on how they manage identity, permissions, policy enforcement, observability, and governance across entire fleets of agents. That is likely where much of the enterprise AI market is headed.
What to do. Treat the harness as production infrastructure.
Budget for least-privilege permissions, identity management, sandboxing, policy enforcement, monitoring, evaluations, human approval gates, rollback, incident response, and cost controls. Those capabilities are not optional scaffolding around the AI. They are what make AI systems trustworthy enough to operate.
Omnigent is still in beta, so this is not a recommendation to place it immediately at the center of your production environment. But it is an important signal. The real question is no longer simply, Which model should we use? It is, What surrounds the model, what authority does it have, and what happens when it is wrong?
Source: Databricks
SIGNAL 03
The AI storm does not change network security. It changes the clock.
What happened. On July 1, the Cloud Security Alliance released "Preparing Your Networks for the AI Storm," guidance for defending networks against AI-accelerated attackers. Its premise: exploit development that used to take months now takes hours, so the risk assumptions behind flat internal networks and slow patch cycles no longer hold. Its refreshing honesty: none of what we built was wrong; AI changes the risks and the timelines.
Why it matters. CSA's answer is not a new product category but the fundamentals of segmentation, patchability, and responsive operations, executed at a pace the old process can't deliver: day-of-release patching for network gear, serial boundaries that force an AI attacker through multiple walls, continuous segmentation instead of one-and-done architecture. Attackers get to focus on one problem; you defend everything. Boundaries are how you make their math worse.
What to do. Run CSA's Phase 1 this quarter: map every internet-facing device, including your security appliances, then rip out end-of-life hardware, patch what is behind, put MFA on management interfaces, and get management access off the internet.
Source: Cloud Security Alliance (report sponsored by Cisco)
THE LESSON
The five questions, read three ways
Every AI use case you approve is shaped by five design questions, whether the team admits it or not. On the surface, these look like engineering questions. They are not only engineering questions. Each one is also a strategy call, a security exposure, and a governance obligation.
How accurate does it need to be? Minimum accuracy determines the level of human review, affects whether the use case enters territory regulators may treat as high-risk, and raises the stakes if someone deliberately induces a wrong answer.
How often will it be used? Usage volume drives both the cost model and the attack surface. A model called a million times a day is not just more expensive. It is a bigger target with a larger blast radius.
Can caching or a cheaper model handle part of the work? Caching can lower cost and improve performance, but it also creates a data store. A cache full of useful answers, user inputs, or retrieved documents is exactly the kind of thing an attacker would love to reach.
Does it have to operate in real time? Real-time operation means no human reviews the answer before a customer, employee, or partner sees it. That moves your safety controls into automated guardrails, monitoring, and rollback procedures.
How much does it need to remember? Every document pulled into the context window is data you now have to protect, and any untrusted content in that context can carry prompt-injection instructions.
Answer all five honestly and you usually land on a smaller model, a narrower scope, a cheaper bill, and a system you can defend and explain. That is not a retreat from ambition. That is what mature AI adoption looks like.
DO THIS WEEK
Answer the five questions three ways
Pick your top proposed AI use case and make a simple table: one row for each of the five questions, and four columns beside them: the strategy answer, the security answer, the governance answer, and an owner. The owner column names who is responsible for the answer, and who is responsible when the answer changes.
This should take an afternoon, not a quarter.
Any blank cell means the use case is not ready, no matter how good the demo looked.
Keep the completed sheet. It becomes the seed of your AI use-case register.
If your strategy, security, and governance conversations happen in three different rooms, that is the gap Hackademic Solutions closes. We walk teams through this intersection in one working session, in days rather than quarters.
— Chris Simpson
Hackademic Solutions
The Hackademic Briefing · Know what's running.