Tag: Data Governance

Meet Your Newest Insider Threat.

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It Doesn’t Have a Leaving Date. It Doesn’t Sleep. And It Has Access to Everything.

Let me introduce you to Dave.

Dave from Compliance is lovely. He’s leaving next Friday, he’s just retired, and he’s just discovered he can sync his entire OneDrive to his personal laptop. Dave isn’t doing this maliciously. He’s just a human with a deeply misguided sense of which files are ‘his’.

Now meet Dave’s replacement. No notice period. No offboarding checklist. No HR signal. It’s an AI agent ….and it’s already inside your environment, reading files, summarising documents, and traversing your SharePoint sites at machine speed. The agent doesn’t mean any harm. But if your data estate is a digital junk drawer with no labels, no classification, and no DLP, it doesn’t need to.

The Insider Risk Problem Just Got a Lot More Complicated

Insider risk has always been a human behaviour problem. The firewall was never the issue. It was always the person on the other side of it. But the conversation has shifted. Dramatically.

The Gurucul 2026 Insider Risk Report, produced in partnership with Cybersecurity Insiders, reports that 94% of organisations say AI adoption is increasing their insider risk exposure, 74% describe that increase as moderate or significant, and 90% experienced at least one insider incident in the past 12 months. These are not niche concerns. That is the entire market sweating through its shirts.

For UK financial services firms, the stakes are even higher. The FCA’s PS26/2 rules on operational resilience set incident reporting deadlines that begin in 18 March 2027, which means that ‘we did not know’ is no longer a defensible position. An Insider Risk Management programme is not a nice-to-have. It is a regulatory expectation wearing a cybersecurity badge.

Enter the Digital Insider

Risky AI usage is no longer limited to someone pasting a sensitive paragraph into a chatbot. The bigger shift is that AI is becoming part of day-to-day business operation. With Copilots, custom agents, and autonomous workflows can now retrieve files, reason across multiple sources, generate summaries from sensitive material, and in some cases take action on a user’s behalf.

Experts describe agentic AI as a new class of ‘digital insider’. Gartner predicts that 40% of enterprise applications will integrate task-specific AI agents by the end of 2026, up from less than 5% today. These agents are not malicious. But they are privileged, persistent, and capable of operating at machine speed. All without a leaving date, a notice period, or a natural sense of ‘I probably shouldn’t be reading this.’

The Microsoft Security Blog cites Microsoft Data Security Index findings that 84% of organisations want greater confidence in managing data input into AI applications, while 78% of users admit to bringing their own AI tools to work. The risk is not theoretical. It is already in your environment.

What Makes Agentic AI Different from Dave

A traditional user might search for one document at a time, open a small set of files, and manually decide what to do next. An agent does something very different.

  • It can query SharePoint sites, Teams content, emails, and repositories in rapid sequence.
  • It can pull fragments from many locations and assemble a high-value answer from data that was never intended to be viewed together.
  • It can surface confidential project plans, customer records, financial forecasts, source code, and credentials stored in long-forgotten collaboration spaces.
  • Security researchers have shown that prompting and retrieval patterns can be abused to turn AI assistants into reconnaissance tools – asking them to identify where passwords, keys, legal terms, or commercially valuable information may exist
    • This is something I’ve tried in multiple clients, a simple search by Copilot to look for documents with the words passwords (these are documents that are shared using ‘People in the Organisation’ option showed a handful of documents in .xlsx and some even in .docx format).

The risk expands further with computer-use style capabilities. Microsoft Copilot Studio’s computer use feature allows an agent to interact with web and desktop applications through a virtual mouse and keyboard. If a person can click through a finance app, enter data into a legacy system, or extract values from a browser session: an agent may be able to do the same. The desktop becomes another high-value discovery and potential exfiltration surface.

The key risk is shifting from simple data exposure to delegated authority. The real concern is not only whether AI can see sensitive data – but whether it can use that data inside connected workflows, across systems, and without the natural limitations of human judgement or working hours.

The Foundation You Cannot Skip

Before you can detect a Digital Insider — human or machine — you need to know what they are after. This is where most programmes collapse: they try to run IRM on top of a data estate that is basically a digital junk drawer.

The foundation is threefold, and none of it is optional:

1. Sensitivity Labels. Your data classification backbone. Without them, every file looks equally important — which means nothing is. In Microsoft Purview, labels like Public, Internal, Confidential, and Highly Confidential give you a taxonomy that both humans and automation can understand.

2. Sensitive Information Types (SITs). The pattern-matching engines that recognise credit card numbers, National Insurance numbers, bank account details, and custom regulatory identifiers. You cannot build DLP or IRM without tuned SITs.

3. Data Loss Prevention (DLP). Your first line of defence. It stops the easy mistakes — the accidental email to a personal account, the unsanctioned USB copy, the public SharePoint link. Crucially, DLP gives you the enforcement muscle before IRM gives you the investigative nuance.

If you do not have these three in place, your Insider Risk Management programme is just a very opinionated alerting system with no teeth.

An AI agent cannot respect a ‘Highly Confidential’ label if no such label exists. DLP cannot block exfiltration of customer data if DLP is not configured. And you cannot investigate anomalous agent behaviour if you have not defined what ‘anomalous’ means for a non-human actor.

Governing the Digital Insider with Microsoft Purview

The governance response has to be equally modern. Once your data foundation is in place, Microsoft Purview provides several layers specifically designed for the agentic AI era:

  • DSPM for AI: Discovers where AI is interacting with your data, identifies oversharing, and surfaces where sensitive information may be exposed to copilots and agents.
  • AI Observability: Extends visibility by showing how agents interact with files and data sources. Without this, you have a Digital Insider with no oversight.
  • DLP for AI Services: Controls what can be pasted, uploaded, or transferred to AI apps and unmanaged destinations. The built-in ‘Generative AI sites‘ group makes this straightforward to deploy.
  • Insider Risk Management (IRM): Adds behavioural context by correlating risky AI prompts, sensitive responses, exfiltration signals, and agent activity into a complete picture of risk.
  • Adaptive Protection — Dynamically adjusts DLP and device control policies based on the risk level of the user (or the agent). A high-risk agent session triggers stricter clipboard, print, and upload controls in real time.

Where to Start: A Practical Checklist

For practitioners, the message is clear. Treat agents as privileged actors, not just productivity features. Here is a practical starting point:

  • Reduce oversharing in SharePoint and other collaboration systems — tighten permissions and audit ‘Anyone’ and ‘Everyone at Organisation’ links.
  • Apply sensitivity labels across your data estate. Use auto-labelling and Trainable Classifiers to catch what manual labelling misses.
  • Deploy and tune DLP policies — covering endpoints, cloud apps, email, and generative AI interactions.
  • Enable DSPM for AI to discover which AI applications are connected to your environment and what data they can access.
  • Configure IRM for agents to detect anomalous behaviour — excessive data access, unusual file retrieval patterns, or attempts to interact with restricted repositories.
  • Build a cross-functional IRM steering committee. HR, Legal, IT, and Business Leaders all need a seat at the table.
  • Publish a clear Acceptable Use Policy and conduct a DPIA before deploying monitoring at scale.

AI Implementation Failures: What We Learned from 2024

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My news feed is filled with “A Year in Review” of what happened in 2024 and the thing that stood out to me was 2024 was a bit of a mess for AI implementations.

From chat-bots giving illegal advice to fake content flooding our news and social media feeds (I’m pretty sure that I’m not the only ones who’ve seen the Pope wear a cool puffy jacket)

Credit: https://x.com/skyferrori/status/1639647708154675200?mx=2

So how did we get here:

The rush to implement AI solutions was largely driven by market pressure and FOMO (Fear of Missing Out). Companies, desperate to stay competitive, rushed to deploy AI solutions without proper governance frameworks or security controls. Board rooms worldwide echoed with demands for “AI strategy,” often without understanding what that actually meant for their business.

This perfect storm was further fueled by the accessibility of AI tools and platforms. What used to require deep technical expertise became available through simple APIs and low-code interfaces. While this democratisation of AI is generally positive, it led to a “wild west” scenario where implementations often outpaced proper security and compliance considerations.

The result? Poor deployment, Terrible user experience and many half-baked AI solutions, security vulnerabilities, and trust issues.

Before You Start: The Boring (But Essential) Bits

Look, I get it – you want to jump straight into the exciting world of AI. But here’s the thing: you need to sort out your data house first. Think of it like baby-proofing your home. Your CISO and security team need to know exactly what data you’ve got, where it lives, and who’s allowed to play with it.

Get your Microsoft Purview DLP policies sorted, tag your sensitive stuff using Purview Information Protection, and make sure you’ve got the right security controls in place. Trust me, this boring bit will save you from some proper headaches later.

The Fix: Four Simple Actionable Steps

  1. Sort Out Your Governance
    • Get an AI committee going
    • Write clear policies on AI usage, Data Protection, etc
    • Set proper standards
    • Actually check if things work (please audit!)
  2. Lock Down Security
  3. Quality Control
    • Keep humans in the loop
    • Test, test, test
    • Watch those outputs (again please run audit checks)
    • Clean data = better results
  4. Smart Implementation
    • Start small, scale later (even on a controlled Copilot for Microsoft 365, pilot it first with a handful of trusted people)
    • Train your people properly, (end-user education is a must)
    • Listen to user feedback
    • Don’t rush it

2024 showed us that rushing in without proper planning is a recipe for disaster. Take your time, do it right, and maybe we won’t see your company in next year’s “AI Fails” list.

Other Sources:

From Novice to Ninja: a new CISOs guide to DLP

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Congratulations, CISO! 🎉 Great job in landing your new role, where protecting sensitive data isn’t just a job—it’s a daily tightrope walk over a pit of cyber threats, compliance demands, and evolving technology.

Now that you’re at the steering wheel, your inbox is probably overflowing with security concerns, regulatory requirements, and a few “fun” audit emails. Don’t worry, you’re in good company. This guide is here to give you actionable steps to set up your Data Loss Prevention (DLP) strategy, ensuring you don’t just survive in this role—you thrive.

So, what does being a CISO mean? Well, you’re now the go-to person when sensitive data sneaks out, malicious insiders get a bit too curious, or someone clicks that suspicious link promising free money from an unknown relative in Timbuktu. No pressure, right? But here’s the deal: inaction is risk. Delaying or overlooking the core elements of a solid DLP strategy could lead to breaches that cost more than your next cybersecurity budget.

To make your journey smoother, I’ve prepared a handy worksheet that you can use right now to take action on your Data Loss Prevention strategy. These aren’t just checkboxes—these are critical steps to lock down your organization’s data and avoid waking up to a breach nightmare.

You can Download the worksheet below.

CISO DLP worksheet

Here’s what you can expect see inside:

1. Classifying Data and Why It’s Important

Why it matters: Not all data is created equal. By classifying your data, you can prioritize resources and security measures where they’re needed most. Would you protect the company picnic plan with the same force as your customers’ financial information? (Spoiler: probably not!)

Example:

  • High-risk data: Customer credit card details, proprietary code, or confidential HR files—things you’d never want to see in the wrong hands.
  • Medium-risk data: Internal meeting notes, marketing strategies—sensitive, but not catastrophic if leaked.
  • Low-risk data: Public reports, customer FAQs—this is the stuff you’d share at a conference.

Take Action Today: Review your organization’s data and start tagging it by risk level. Ask yourself, “What would happen if this data got out?” and use that to guide your classification efforts

2. Why and How to Identify Sensitive Data

Why it matters: You can’t protect what you don’t know exists. Sensitive data is often hidden across different platforms—sometimes even in the most unexpected places (like a random email attachment or NTFS file shares). Identifying it is the first step to ensuring it stays secure.

Example:

  • Sensitive Data: Personally Identifiable Information (PII) like social security numbers or health records, intellectual property (IP), and anything that’s subject to regulations like GDPR or HIPAA.
  • Surprise Discovery: Finding a list of client emails attached to a forgotten project buried in a shared folder.

Take Action Today: Use a discovery tool or audit your data manually. Start with cloud storage, email servers, and shared folders. Look for data that could lead to a privacy violation or financial loss if exposed.

Photo by cottonbro studio

3. Developing a Data Handling Policy

Why it matters: A solid data handling policy is the foundation of your DLP strategy. Without clear rules in place, sensitive information can slip through the cracks, exposing your organization to unnecessary risk. Your data handling policy ensures everyone—from top execs to interns—understands the dos and don’ts of handling sensitive information.

Example:

  • Clear Guidelines: For high-risk data like financial information, the policy might mandate encryption during transfer and restricted access to authorized personnel only.
  • Real-Life Scenario: Imagine your marketing team accidentally sharing a file with customer details over an unsecured network. A proper data handling policy would prevent this by enforcing secure file transfer practices.

Take Action Today: Draft a policy that covers how different types of data (high, medium, low risk) should be handled. It should specify everything from encryption requirements to access control and data retention periods. Involve key stakeholders (Legal, IT, HR) to ensure all bases are covered.

Now that you know the key steps to securing your organization’s data, it’s time to plan it out, partner with your internal stakeholders, and take action. DLP isn’t a one-person job—it’s a team effort that involves collaboration across IT, Legal, HR, and beyond. The risks of inaction are far too high, so don’t wait until something goes wrong. Proactively implementing these best practices today will not only protect your data but also strengthen your leadership as a new CISO.