Imagine an AI that doesn't just chat, it clicks buttons. It books appointments, files tickets,

updates records and pings APIs. It's not a chatbot anymore. It's a doer.

And that's agentic AI.

And here's the kicker. The biggest risk isn't just what it thinks, it's what it does. Especially

when shadow AI pops up outside the lines.

If security and governance live on different islands, that doer can outrun your oversight in

seconds. So we need one cockpit, one radar, one living picture of risk that follows

every move an agent makes. Quick pit stop. What's shadow AI and why should we

care? Shadow AI is the unofficial AI your team spin up to get things done with no tickets, no

approvals and no paper trail. Think of it as the well-meaning intern who quietly built a

production app on a lunch break. It starts as a tiny helper, a script here, a model there, an

agent wired to a SaaS tool. And suddenly it's talking to customer data. Calling third-party

APIs and writing to systems nobody is officially tracking. Why is this a big deal?

Well, first of all, it's hard to see. It's hard to see if people

don't know a robot helper exists. They can't keep it safe. Imagine a new puppy in the house that no one

told your parents about. No leash, no food bowl and doors left open. The puppy can run outside or chew

things. Well, hidden tech can run outside too. Second, it's easy to

leak. Some helpers copy and paste things or use loose keys like

passwords. If those aren't protected, private info can slip out. Think of writing your home address

on a balloon and letting it go. Anyone can read it. We want the balloon tied down.

Third is the trouble with compliance.

Teams have to show we follow the rules. If there is no record of what the helper did, it's like

turning in homework with no name and no steps shown. When the teacher, the auditor, asks, "How did

you get this answer?", you need to show your work. And fourth, there's too much access.

Giving a helper every permission just for now is like giving a friend the keys to your whole house

when they only need it to water one plant. If something goes wrong, they can open every door. We

should give the smallest key that does the job. And five, messy incidents tend to

happen. When a hidden helper breaks, people don't know who owns it, what

it touched or how big the mess is. That's like spilling paint and not knowing which room it came

from. Cleanup takes longer because you're searching for every room. And last is about

what you do, instead. You need to tell someone before you add a new

helper. Write down what it can do and what it can't do. Give it only the keys it needs. Keep a

small log of what it did, like a chore chart. So if something spills, you can clean it up fast.

All right, now back to our flight plan. Think air traffic control for AI.

First, you see every aircraft, including the unscheduled ones, continuously discover

shadow agents lurking in any reports, cloud projects and embedded systems and pull them under

oversight automatically. Then stress test the plan with automated red teaming.

Probe for prompt injection, data leakage, tool misuse and brittle configurations before

attackers do. Next, you enforce runtime policy: least privilege tool access,

guardrails on inputs and outputs, and active monitoring for risky data moves. Everything tied

back to a single re ... risk register both security and governance can act on.

Finally, you will use automated logging and controls to generate actionable evidence for

every AI action. And that's the shift: from scattered

checklist to one control plane for agentic AI. You discover,

assess; then you govern, you secure,

and last, you audit. In one

continuous loop. Do this well and you don't just cut

incidents, you speed up safely. Right now, the fastest way to scale AI is the safest way.

Unify how you see risk. Unify how you control it, and keep your agent honest every step of the

way. All right, buckle up and let's see where this gets real in two use cases: one in healthcare

where AI meets patients, and the other in the public sector where it serves citizens. The first

use case is about how AI enables patient care, and specifically why guardrails do matter.

Imagine the following scenario. So regular afternoon clinic slot. The patient sits down, a

little anxious with a list of symptoms on their phone. The room is calm. No frantic typing, no

screen between them and the clinician, just a small consented mic on the table and the

clinician's full attention. The conversation feels unhurried. The clinician asks follow-up questions,

makes eye contact and reflects key points back in plain language. The patient notices the

difference. They don't have to repeat themselves, and they actually feel heard. Behind the

calm, the agent is working quietly. As the patient

talks, it turns the dialog into a draft note. Double checks facts against the chart, flags.

anything that doesn't line up, proposes orders, lines up the follow-up, and prepares a friendly

after-visit summary. Nothing is final without the clinician's approval, but the busy work is already

handled. To the patient, it feels like the system finally got out of the way so the human care

could come through. Here are five things that actually happen under the hood and how

things stay safe. First, the agent turns the conversation into a tidy,

clinical note, including history, meds,

allergies, the assessment, and because it was evaluated before rollout, for accuracy and

faithfulness. It knows when not to over-summarize, anything uncertain is clearly flagged for a quick

human review, so the record stays trustworthy. Second, when the agent hears

Metformin 1000mg, but the chart actually shows 500mg, it raises a

clear mismatch for the clinician to confirm or fix. And because

it only has read-only least-privilege access, it can compare facts and draft a correction, but

cannot silently change the medication list on its own. Third, before any order

is placed, the agent runs drug and allergy checks and prepares everything as a draft.

While governance policies require a human in the loop, ferments and procedures and log any

exception with the reason, so speed never outruns clinical safety. Fourth, the agent

pre-stages the follow-up appointment and referral

paperwork and prior authorization with one-tap approvals, and each connected tool runs with only

the minimum permission it needs. Documentation can't export bulk records, scheduling can't see

billing, so useful automation doesn't turn into broad access. Developers can implement these

guardrails through APIs, permissions and audit logging frameworks. And five,

a patient-friendly plan with reminders is generated and every

instruction links back to its approved source in the note, making it super easy for staff to verify

or correct in seconds, and ensuring patients leave with guidance that's both clear and auditable.

Our next example dives into the world of citizen services. Think about everyday interactions, simple

tasks that can reveal a lot about user experience and government efficiency. Okay, imagine the

following scene. It's a Saturday morning. A citizen opens the state services app on their phone to

finish two chores at once: file their state taxes and renew a fishing license for the new

season. The interface is simple: one chatbot with optional voice. The tone is calm

and human. The assistant explains what it will do, ask for consent and confirms identity once.

No MESA Forms, no guessing which website is actually the right one.

Behind the com, an agent is working quietly.

It understands the request, pulls only the records it needs, fills in the blanks, warns about anything

risky and prepares the final steps for approval. Nothing is submitted or paid without the citizen's

okay. To the citizen, it feels like the system finally got out of the way so they can just get

things done. Let's take a look at the five things that actually happen under the hood and how it

stays safe while they happen. First, the assistant confirms identity

and asks for consent to access specific records for taxes and licensing, then limits its own reach

to just those systems so it can answer the questions without dipping into unrelated data.

This keeps the task focused and protects privacy by design. Second, the agent

retrieves last year's filing, current employer reported income and payment history.

And for licensing, it checks residency, prior license status, and any required education or

catch limits. The assistant shows what sources it used in plain language, so the citizen can see

where the information came from and correct anything that looks off. Third, the agent

prepares a tax summary with

line items, credits and estimated refund or amount due. And for the fishing license, it prefills the

renewal form and explains any new rules for the upcoming season. Key choices are highlighted and

explained in simple terms, and anything uncertain or unusual is flagged for the citizen to review

before moving on. Fourth, when the citizen is ready to submit and pay, the agent

uses least-privilege access to create a filing and a license renewal draft,

then calls the payment system only with the minimal details needed to process the transaction.

Prompts and outputs are filtered to prevent personal data from leaking to the wrong place, and

risky tool calls are blocked and logged automatically. And finally, after

the citizen approves, the filings are submitted. Receipts

are issued and reminders are set for future deadlines. The system records what was accessed,

which rules were applied, the versions of the models used and what the citizen approved,

producing an audit trail that logs every action and ensure full traceability. In the end, this

isn't about showy demos or shiny dashboards. It's about running AI that actually gets work

done safely, predictably and without creating tomorrow's crisis. Agents don't just

chat anymore, they act. They click buttons. They

move data. And they spend

money. That means the real risk isn't what they say, it's what they

do. If you can't see those actions, test them, control them and prove them, you're flying fast

in fog. Here's the reality: shadow AI over here

will show up whether you plan for it or not. Visibility isn't optional; it's oxygen. You

have to discover everything, especially the tools no one officially approved. Then make red

team by default your new normal. So prompt tricks and over-permission agents get caught in

rehearsal, not splashed across headlines. Least privilege is your seatbelt. Every

agent gets only the keys it needs, nothing more. When something fails, the damage stays small,

understandable and fixable. Pair that with the live monitoring, and mystery outages turn into

quick recoveries instead of week-long investigations. And remember, evidence beats

promises every time. If you can show which data was used, what rules fired, who approved and

what version ran, audits take minutes, not months. That's how you earn trust—from patients, citizens,

clinicians and caseworkers who just want systems that stay calm when things get hard. For

healthcare, the win is human. More eye contact, fewer clicks, safer orders, cleaner

handoffs. For the public sector, the win is trust. Clear guidance, faster service, fewer fraud

losses and records that hold up under pressure. Here's the move: bring security

and governance into one cockpit. Run the loop

continuously. Discover, assess, govern, secure, audit. And do it the same way every

time. You won't slow down. You'll go faster because you're safer. That's how agentic AI

grows up, and how we keep control of the systems that now act in our name.