RichardHam.co.uk Blog

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  • Why Dependency Hygiene Becomes a Leadership Issue

    Why Dependency Hygiene Becomes a Leadership Issue

    A lot of teams still treat package upgrades and release housekeeping as purely technical chores.

    That made sense when the application estate was simpler, the change rate was slower, and a missed patch mostly meant a slightly awkward sprint later on. It makes much less sense once your organisation depends on AI services, automation workflows, customer-facing portals, and a growing set of third-party components that can change under your feet.

    This week’s GitHub activity was a useful reminder of that.

    Across several public repos, the work was not centred on a dramatic new feature. It was centred on the quieter jobs that keep live systems usable: upgrading a framework across multiple sites, tightening the way an agent platform distinguishes billing exhaustion from authentication failure, restoring an explicit safety guard with regression coverage, and shipping deployment handover notes alongside a website redesign.

    That mix matters because it points to a broader operating reality: dependency hygiene is no longer just a developer preference. It has become part of leadership discipline.

    The recent GitHub signals were small, but not trivial

    A few examples stood out.

    One public media/website repo recently went through a cluster of framework upgrades across multiple surfaces. On the face of it, those are dependency bumps. In practice, they are evidence of someone choosing not to let the front-end estate drift quietly into a harder future upgrade.

    Another public analytics/cost-tracking repo saw updates to numerical and coverage tooling. Again, this is the sort of work many leadership teams never see. Yet if cost reporting or analytics underpin operational decisions, stale numerical libraries and test tooling are not invisible engineering details. They affect how confidently the team can rely on output.

    In one public agent platform, a recent change separated billing exhaustion from a generic authentication failure, while another restored a missing enabled-guard and added regression tests. Those are strong examples of operational maturity. The first makes failure states easier to understand. The second makes it harder for a disabled service path to behave ambiguously.

    Then there was website delivery work in a public site repo: one change added a deployment handover document, and another shipped the full site build and deploy tooling. That is not just design delivery. It is an acknowledgement that shipping is only half the job if nobody else can support the result safely afterwards.

    Why senior leaders should care about this work

    If you are a founder, managing partner, COO, CIO, or board sponsor, none of this should be dismissed as back-room maintenance.

    Once AI, automation, or client-facing digital journeys are part of the operating model, dependency hygiene becomes a business issue for three reasons.

    1. Drift increases the cost of every later decision

    Technical drift rarely fails all at once. It accumulates quietly.

    A framework stays two major versions behind. A library update gets deferred because it is inconvenient. A brittle edge case stays undocumented because the team who understands it is still around. Nothing looks urgent until a new feature, security fix, or partner integration suddenly depends on clearing months of deferred maintenance.

    That is when the bill arrives.

    For SMEs and founder-led businesses, that usually shows up as delivery drag. For law firms and healthcare organisations, it can also create assurance problems because the stack behind a client-facing process becomes harder to explain. For PE-backed companies, it turns into diligence friction. If the technology estate looks under-maintained, every transformation claim starts to feel less believable.

    2. Poorly classified failures waste leadership attention

    One of the most useful repo changes this week was not a new feature at all. It was the decision in one public agent platform to separate billing exhaustion from a generic auth failure.

    That matters because leaders make bad decisions when the system reports the wrong category of problem.

    If a model provider has hit a spend limit, that is a budget, quota, or supplier-management conversation. If the credentials are wrong, that is a configuration or secrets-management conversation. If both are lumped together as “auth failed”, the team loses time, the escalation path gets muddled, and confidence in the monitoring starts to erode.

    At scale, this is one of the easiest ways for senior people to get dragged into operational noise that should have been designed out earlier.

    3. Handover quality is part of risk control

    I think many organisations still underestimate how much risk sits in the gap between “it works” and “someone else can run it”.

    That is why the deployment handover work in dh-electrical-uk-website is commercially important. When a redesign lands with deploy tooling and supporting handover notes, the delivery is stronger than a visually successful launch on its own. It means the service is more likely to survive staff changes, supplier changes, holiday cover, and the inevitable moment when something needs to be updated under time pressure.

    In practical terms, handover is where a lot of hidden fragility gets exposed:

    • which versions are actually supported
    • which environment assumptions are undocumented
    • which steps still live in one person’s head
    • which recovery actions have never been written down

    If that sounds familiar, the issue is not documentation style. It is operating-model debt.

    What good leadership looks like here

    The answer is not for non-technical leaders to micromanage every package bump.

    The answer is to treat upgrade hygiene, failure clarity, and supportability as visible management concerns. In practice, I would want four things.

    Keep an explicit tolerance for drift

    Not every dependency needs to be latest immediately. But the organisation should know what level of lag is acceptable, where major-version changes are being deferred deliberately, and who owns the call when a deferral becomes risky.

    Insist on failure signals that guide action

    If an automation platform cannot distinguish billing, auth, config, runtime, and policy problems cleanly, the team will spend too much time diagnosing symptoms and not enough time fixing causes.

    Ask for handover evidence before calling a project done

    If a supplier, internal team, or blended delivery setup cannot show you the deploy path, the support notes, and the recovery assumptions, you do not yet have a finished operational asset.

    Connect maintenance discipline to commercial credibility

    Clients, investors, regulators, and acquirers do not usually ask whether numpy or Astro was bumped on Tuesday. They do care whether your digital services are dependable, whether your reporting is trustworthy enough to guide decisions, and whether your systems can be maintained without heroics.

    That is the commercial expression of dependency hygiene.

    A simple question worth asking this quarter

    If your organisation relies on AI, automation, or a modern web estate, ask one blunt question:

    Which live services would become awkward or risky to change if the current operator disappeared for two weeks?

    The answer usually reveals more than a tooling audit alone.

    It shows where drift has been tolerated too long, where failure messages are too vague to manage well, and where handover has been assumed rather than designed. Those are exactly the places where fractional leadership earns its keep, because the problem is rarely just a code problem. It sits across governance, service design, supplier control, and risk appetite.

    If that question exposes uncomfortable gaps, that is useful. It gives you a practical starting point.

    If you want help turning that picture into a calmer operating model, my services cover the overlap between security leadership, IT direction, and AI architecture. Or get in touch if you want a senior review of where maintenance debt, weak handover, or ambiguous failure handling is making change harder than it should be.

  • AI Agent Audit Trails: What to Record Before the Auditor Asks

    Once an AI agent starts taking actions — updating records, sending messages, moving money, calling APIs — the question changes from “is the output good?” to “can you prove what happened?”. Most teams can answer the first question. Very few can answer the second, and that gap tends to surface at the worst possible moment: an ISO 27001 audit, a cyber insurance renewal, or a customer’s security team working through a supplier questionnaire.

    We have written before about why audit trails matter for agentic AI. This piece is about the practical side: what your log actually needs to contain, where it needs to live, and who reviews it.

    Why an agent changes the logging picture

    Traditional application logs record what your software did. An AI agent is a different animal, because three things are running at once: a model making probabilistic decisions, a workflow engine executing steps, and tools acting on live business systems. A log that captures only the final action cannot answer the questions an auditor will ask.

    Specifically, an audit trail for an AI agent run needs to answer:

    – What did the agent see? (the input, including any retrieved context or documents)
    – What did it decide, and with which model and agent version?
    – What did it actually execute, step by step?
    – Who approved it, if a human approval was in the loop?
    – What changed in the underlying systems, before and after?

    What to record for every run

    At minimum, each execution should capture:

    – Identity: which agent ran, which version, which model underneath it. “The AI did it” is not an audit answer; version 2.4 of your triage agent acting through a named model is.
    – Input: what the agent received — the message, document, or trigger, including any context retrieved from your knowledge base. Retrieval-augmented agents are only as auditable as their sources.
    – Tool calls and parameters: every external action the agent took, with the parameters passed. This is usually the line of evidence that reconstructs an incident.
    – Before and after state: what a record looked like before the agent edited it and after. Most disputes reduce to this.
    – Approvals: any human sign-off in the chain, tied to a named person or role, with a timestamp.
    – Outcome and errors: what completed, what failed, and what the agent did in response to the failure.

    Append-only storage matters here. If the log can be edited after the fact, an auditor will treat it as a claim rather than evidence, and they will be right to.

    The UK compliance angle

    Two obligations make this more than good hygiene.

    The UK GDPR accountability principle means you must be able to demonstrate how personal data is processed — and an agent updating customer records is processing, whether or not a human clicked the button. Your records of processing activities should reflect automated agents as processing operations, not sit silently beside them.

    If ISO 27001 is in scope — increasingly a condition of enterprise contracts — logging and monitoring are explicit controls in the standard’s Annex A. Auditors do not accept “we use a platform that probably logs things”. They ask to see the log, the retention policy, and the review routine.

    Insurers are moving the same direction. When a cyber renewal asks how you detect and investigate unauthorised changes, an AI agent audit trail is fast becoming part of the answer.

    Who checks the log, and how often

    A log nobody reads is an expense, not a control. Two routines cover most SME needs:

    – Monthly sample review: pull a handful of agent runs at random, read them end to end, and confirm the actions match the intent. This catches drift — a prompt that has slowly degraded, a tool permission nobody meant to grant.
    – Exception alerting: define the actions that should never happen without a named approver — refunds above a threshold, deletes, anything touching privileged systems — and alert when they occur.

    Someone has to own both routines. Not “the team”, not the agent platform. A named person.

    Red flags worth fixing now

    – Your logs record prompts but not the actions the agent executed.
    – Agent runs are not versioned, so you cannot tell which behaviour produced an old result.
    – The audit trail lives in a system the agent itself can modify.
    – Human approvals are recorded as a generic “approved” with no identity attached.
    – Nobody can name the retention period for agent logs, because nobody chose one.

    None of these require a big programme to fix. Structured logging, append-only storage and a named owner get you most of the way there, and the tooling is ordinary — the discipline is the hard part.

    If your team is rolling out agent workflows and you want an independent view on what an auditor, insurer or enterprise customer will expect to see, get in touch via the contact page.

  • Fractional CISO vs Virtual CISO: What UK SMEs Actually Need

    Search for security leadership support for a UK SME and you will hit two terms: fractional CISO and virtual CISO. Providers use them interchangeably, job ads mix them up, and most comparison content on the internet was written to fill a gap rather than to explain the market.

    Here is the honest answer: in the UK market, these two terms usually describe the same thing. A senior security leader working with your business on a part-time, retained basis. If you are comparing providers on their job title alone, you are comparing the wrong thing. What actually differs is how the engagement is structured — and that is where SMEs get this decision right or wrong.

    Why the terms get used interchangeably

    Both models exist for the same reason: most SMEs do not need a full-time CISO. A permanent CISO in the UK runs roughly £120,000 to £180,000 a year, and most businesses under a few hundred staff cannot justify that role five days a week. What they need is serious security judgement in the room a few days a month — someone to set direction, own risk, and answer to the board, insurers and customers.

    Both fractional and virtual CISO models deliver exactly that. Some providers call two retained days a week a fractional engagement; others call the same arrangement virtual. There is no standards body defining the terms, so the label tells you less than it appears to.

    What the engagement actually looks like

    Rather than comparing labels, compare the shape of the arrangement. These are the dimensions that genuinely differ between providers — under either name:

    – Days and rhythm: how many days a month, and whether they are fixed (for example, the first Tuesday of each month) or drawn on demand. Fixed rhythms produce better governance; on-demand arrangements tend to react rather than lead.
    – Scope: is the engagement strategy-only, or does it include hands-on work — configuring controls, reviewing your Microsoft 365 baseline, running supplier assessments? Some leaders stay at board level; others roll up their sleeves. Both are valid, but you should know which you are buying.
    – Deliverables: a security strategy, a risk register that actually gets updated, policy set, an incident response plan your team can follow, evidence packs for Cyber Essentials or ISO 27001. If a provider cannot list what lands in your inbox each month, you are buying a retainer, not an outcome.
    – Presence: do they attend your board or leadership meetings? Do they speak to your insurers or your largest customer’s security team when a supplier assurance questionnaire arrives? That conversation is often the real job.
    – Cover: what happens when something goes wrong at 4am, or while your CISO-equivalent is on holiday? Some engagements include incident cover; others do not.

    What UK SMEs should actually compare

    When you are weighing two proposals, these questions separate a genuine security leader from an expensive pair of hands:

    – Regulatory grounding: can they explain how your UK GDPR accountability obligations apply to the systems you actually run, including any AI tools your team has adopted? A good fractional CISO reads your record of processing and tells you what is missing.
    – Certification pathway: if a customer or insurer has made Cyber Essentials Plus or ISO 27001 a condition of doing business, do they own that programme end to end — gap assessment, remediation, internal audit, certification support? This is where part-time security leadership earns its fee.
    – Evidence habits: insurers and enterprise customers increasingly ask for evidence, not assurances. Do they build the logging, reporting and review routines that produce that evidence continuously, or scramble for it when a renewal lands?
    – Honesty about fit: a good provider will tell you when you do not need them yet. If every conversation pushes you towards a bigger retainer, treat that as a signal.

    A realistic progression for a growing SME

    A pattern I see repeatedly, and one worth planning for:

    Early stage, you bring in fractional support to build the foundations — strategy, policy set, a risk register, a Cyber Essentials submission. A few days a month is enough.

    As you grow, customer scrutiny grows with you. Enterprise buyers start sending supplier assurance questionnaires. Cyber insurance renewals start asking harder questions. The monthly rhythm that worked at twenty staff leaves gaps at a hundred, and the engagement shifts towards a more continuous virtual model — more days, more operational presence, real incident cover.

    The businesses that handle this well treat it as one relationship maturing, not two different services. The ones that struggle rip up the arrangement each time their circumstances change, usually under time pressure from a customer deadline.

    The short version

    Fractional CISO and virtual CISO are two labels for an overlapping market. Do not choose between the terms — choose the provider, and compare them on days, scope, deliverables, presence and cover. Ask what evidence they will produce for your insurers, your customers and your auditors. And if you are planning an AI pilot alongside all this, make sure whoever takes the security lead can talk about that too, because it is now a board question, not a technical one.

    If you are weighing up security leadership support for your business and want a straight view on what you actually need, get in touch via the contact page.

  • Hermes in the Enterprise: A Security Team’s Threat Model

    Hermes Agent is heading for enterprise networks. On 7 October 2026 the Wall Street Journal reported that Nous Research had raised $90 million at a $1.5 billion valuation to take its open-source agent from individual developers into corporate environments — the article notes more than 22 million downloads since February. This is no longer a tool your most curious engineer runs on a spare VM. It will be bought, piloted and integrated by organisations that answer to auditors, insurers and customers.

    If your team is evaluating Hermes or any agentic AI of this class, what should your security function ask first? Not “is it safe?” — that never gets a useful answer. The right questions: what can this thing do without me, who can talk to it, and how would I know when it did something I would not have approved?

    What Hermes actually does

    Hermes Agent is an autonomous agent from Nous Research, the open-weights lab behind the Hermes model family — not a chatbot bolted onto an API. It uses tool calling to read and write files, run shell commands and code, browse the web, call external APIs and model context protocol servers, and spawn isolated subagents. It runs on terminal backends from your own machine to Docker containers, remote SSH hosts or serverless sandboxes.

    Two features matter most for a threat model. First, the learning loop: Hermes creates and updates its own skills — reusable procedural documents it loads when a task matches — and the official docs state the agent can modify or delete any skill, with human-approved skill writes as an optional gate that is off by default. Second, the messaging gateway: the same agent is reachable from Telegram, Discord, Slack, WhatsApp, Microsoft Teams and more, with built-in scheduling so it runs unattended jobs around the clock. You can message it from your phone while it executes on a server you never log into.

    That combination — persistent autonomy, self-modifying procedure, conversational remote control — is genuinely useful, and a prompt-injection class in production form.

    The threat model

    Prompt injection via web content and files. Hermes reads what you point it at: web pages, documents, repositories, emails. Instructions hidden in that content are indistinguishable from instructions you gave it. This is not hypothetical — in July 2026 researchers published a proof of concept, reported by The Hacker News, showing leading coding agents tricked into executing attacker-supplied binaries via a poisoned README in an open-source repository. Hermes has mitigations — context-file scanning, always-on request forgery protection blocking fetches to private networks, a configurable website blocklist — but the scanning is explicitly heuristic, not semantic. Treat web and file content as untrusted input to a privileged process.
    Malicious or drifting self-created skills. Skills are the agent’s procedural memory, drawn from bundled sets, community hubs, or the agent writing its own. A skill is a markdown document that shapes future behaviour — instructions to ignore approvals, exfiltrate data or quietly widen scope would persist until someone reads the file. Default configuration writes skills freely, and a skill that was correct in March can drift as the environment changes.
    Chat account takeover. Hermes treats whoever can message the gateway as the operator. An attacker who compromises a linked Telegram or Discord account inherits the agent’s access. The gateway has layered authorisation — per-platform user allowlists, pairing codes, deny-by-default when nothing is configured — but consumer chat platforms are not your identity provider.
    Secrets exposure. The agent holds model API keys and can be handed cloud, email and code-hosting credentials. Redaction exists for tool error output, but anything the agent can read — environment files, notes, logs — can end up in a model prompt, a chat message, or a file it writes somewhere public.
    Over-broad permissions. With dozens of built-in tools and shell execution, a default install is a small operating system with your name on it. YOLO mode exists and disables approval prompts entirely; so does flipping approvals off in configuration. Headless surfaces default to denying dangerous commands, but defaults are a floor, not a control set.
    Weak audit trail. Hermes logs locally and keeps session history, but if your security operations team cannot query it, you do not have an audit trail — you have files. An agent acting every few minutes needs central logging before it needs a use case.

    Controls that actually apply

    Most of these exist in Hermes today. The gap in most pilots is that nobody configures them.

    Sandbox execution. Run command execution on the container backend — which drops Linux capabilities, blocks privilege escalation and limits processes — or on a dedicated host behind an SSH backend. Never a domain-joined workstation.
    Gate skill writes and new skills. Turn on the skill write-approval gate so every skill the agent creates or edits is staged for human review. Review staged skills with the seriousness you give code changes; functionally they are.
    Least-privilege credentials. Give the agent scoped keys with hard spend caps, separate from any human admin account. Never one credential that unlocks both infrastructure and production data.
    Disable consumer chat channels, or fence them properly. If the pilot needs messaging, use a dedicated workspace with explicit user allowlists and pairing codes; if not, run CLI-only. Do not bridge an enterprise agent to a personal Telegram account.
    Central logging. Ship gateway and session logs to your existing log platform on day one. Log authorisation denials, approval decisions and skill writes at minimum.
    A kill switch that is really a switch. Hermes does not ship a single named panic button; build one. Stopping the gateway service, revoking platform tokens and disabling API keys are three independent ways to stop it — documented and tested, and placed outside the agent, since Hermes deliberately blocks self-restart from inside its own supervised process.
    Keep it updated, and say no to the easy paths. Run updates for security patches, keep allow-all-user flags off in production, and treat “just disable approvals for the pilot” as the decision it really is.

    Field notes

    I run Hermes myself across my own operations, so these come from real deployments, not lab speculation. Tool names stay; internal infrastructure stays out.

    – Secrets do end up in transcripts. A dedicated scanning and rotation process found hundreds of credential-shaped findings in Hermes session transcripts over a few months — API keys and bearer tokens reaching cloud model contexts through normal tool use. Hermes redacts tool error output, but that is one layer; what the agent reads can still reach a model prompt. Scanning after the fact is no substitute for not feeding it secrets.
    – Skill self-management is double-edged. The agent’s ability to create and edit its own skills is useful — and it also meant every profile loaded the full skill catalogue regardless of relevance, wasting tokens and polluting context with instructions for domains that did not apply. Per-profile isolation was needed to fix it. If the agent writes its own procedures, someone has to review what accumulated.
    – Approval gates create friction — and that friction is the point. My approval gate blocking routine cleanup commands (think `rm -rf` on temp directories after agent work) caused files to pile up. The fix is not to weaken the gate; it is to document an approved alternative path. If your team starts asking to disable approvals to “get things moving”, you have found the real control.
    – Monitoring the agent matters as much as the agent itself. My own budget-and-spend guard cron fired a false critical alert for days because of a logic quirk — reading the wrong figure as budget exhaustion. Automated agents generate automated noise; if nobody owns the monitoring that watches the agent, false alarms and real ones look identical.

    Pre-deployment checklist

    Before Hermes (or anything like it) touches enterprise data:

    1. Written threat model covering injection via every content source the agent reads.
    2. Command execution sandboxed — container or dedicated host, non-root, resource-limited.
    3. Skill write-approval enabled; a named human reviews staged skills.
    4. Credential inventory complete; agent keys scoped, capped and separable.
    5. Chat channels either disabled or on allowlists behind a dedicated workspace.
    6. Central logging live and queried at least once before go-live.
    7. Kill switch documented, owned and tested from outside the agent.
    8. YOLO and approval-off modes blocked by configuration review.
    9. Update and patch cadence assigned to a named owner.
    10. A rollback: what you will do when the first incident happens.

    Most of this is unglamorous. None of it is optional. Agentic AI rewards teams that treat the control plane as the product.

    If your team is evaluating agentic AI and wants a second pair of eyes on the threat model before a pilot starts, my services page covers security and security strategy work for UK businesses — or get in touch and we can talk through where you are.

    Sources

    – WSJ Pro, “Nous Research Scores $90 Million to Bring Open-Source AI Assistant to Enterprises”, 7 October 2026 (funding figure, valuation, enterprise strategy, download count).
    – Hermes Agent official documentation, Nous Research: overview, skills system, security user guide, messaging gateway.
    – The Hacker News, “Top AI Agents Built to Catch Malicious Code Can Be Tricked Into Running It”, 9 July 2026, covering the AI Now Institute “Friendly Fire” proof of concept.

    Flagged, not stated as fact: “kill switch” is operational framing, not a documented Hermes feature; prompt-injection evidence cited is from competing agents; download counts are vendor-reported via WSJ.

  • Before You Launch an AI Assessment, Fix the Operating Model

    Before You Launch an AI Assessment, Fix the Operating Model

    A lot of AI advisory offers now start with an assessment.

    The problem is that many of these offers are still being built like marketing assets rather than client-facing systems.

    If the output influences buying decisions, budget allocation, compliance posture, or board discussion, the operating model behind it matters as much as the prompt or the front end. Recent GitHub work around an AI consultancy assessment build, plus reliability work in adjacent automation repos, makes that obvious.

    The recent signals are not about polish alone

    One assessment build in particular stood out this week.

    The visible activity was not just about shipping a shiny MVP. It included concrete follow-up work such as:

    • provider fallback handling for report generation
    • safer rendering of generated reports and clearer visitor handoff paths
    • lead email notification flows
    • privacy, GDPR, and security controls
    • a protected admin dashboard for internal review

    That list is commercially useful because it shows the build moving away from “can we generate an AI report?” and toward the harder question: “can we run this as a client-facing service?”

    The same instinct showed up elsewhere. In one public agent platform, recent changes added approval requirements around sensitive gateway replacement paths and tightened behaviour when an API server is explicitly disabled. In another internal management context, recent work also focused on surfacing failed or missing configuration steps as real errors rather than false-green output.

    An AI assessment is a service, not a content asset

    This is the point many firms miss.

    An AI assessment may arrive through a landing page, but from the user’s point of view it behaves like a service. It collects inputs. It processes them. It generates output that may shape strategy. It creates follow-up work for your team. It may retain commercially sensitive information. It may trigger email workflows or lead handling.

    That means buyers, especially in law firms, healthcare organisations, PE-backed businesses, and regulated SMEs, will judge it on more than whether the wording feels intelligent.

    They will care about questions such as:

    • What happens if the model fails halfway through a report?
    • Where does the submitted information go?
    • Who can see the results internally?
    • Will someone follow up while the lead is still warm?

    Those are operating-model questions. If they are answered late, the launch becomes fragile. If they are answered early, the assessment becomes a serious commercial asset.

    The four controls I would design before launch

    When I look at the recent issue set, I see four controls that should exist before any AI assessment is treated as production-grade.

    1. Fallbacks for report generation

    Provider fallback handling is one of the first giveaways that the team is thinking properly.

    If your report workflow depends on a single provider, a temporary outage or degraded model response can turn a promising user journey into a dead end. A fallback model path does not need to be elaborate on day one, but it does need to exist. You should know:

    • which provider is primary
    • which fallback path is acceptable
    • how quality is checked before the output is shown
    • what the user sees if both paths fail

    2. Safe rendering and an explicit handoff path

    Safe rendering and an explicit handoff path may sound like front-end housekeeping, but they are more important than that.

    AI-generated output often carries awkward structure, inconsistent formatting, and the occasional sentence that reads far more confidently than the evidence supports. Treat output rendering as a control surface: sanitize it, structure it, keep the language disciplined, then give the reader a clear next step.

    A practical CTA is part of the safety model here, not just the conversion model. If the output is intended to open a commercial conversation rather than substitute for expert judgement, the interface should say so and point naturally to the services page or the contact page.

    3. Privacy, GDPR, and role-based access

    For UK buyers, especially in legal and healthcare environments, privacy, GDPR, and role-based access are where a build starts becoming credible.

    An assessment tool often collects exactly the sort of operational detail that organisations do not want sprayed across logs, inboxes, and loosely protected admin views. Decide early:

    • what data is stored
    • what is redacted or minimised
    • how long submissions are retained
    • which internal roles can access raw answers
    • what the lawful basis and privacy notice look like

    A protected admin dashboard belongs in the same conversation. Internal convenience is not a good enough reason for weak access control.

    4. Truthful monitoring and approvals around the edges

    The adjacent repo activity matters here because it reinforces a broader discipline.

    If a nightly check can go false-green, or a gateway action can happen without the right approval, your delivery stack is already telling you something about risk appetite. Public agent-platform fixes and internal management work both point to the same lesson: the system around the assessment needs honest signals and controlled change paths.

    For a buyer-facing AI assessment, keep one rule in mind: do not automate yourself into ambiguity.

    If emails fail, surface it. If a fallback is used, log it. If an admin action changes routing or content, require the right level of review. If an integration is disabled, behave safely and obviously rather than trying to muddle through.

    That is how you keep confidence high without pretending the system is infallible.

    Where this lands commercially

    This is not only a product design issue. It affects how the market reads your firm.

    A well-run assessment signals seniority. It tells a prospect that you understand not just AI tooling, but governance, service design, delivery risk, and follow-through. A weakly controlled assessment suggests the front-end story is outrunning the operating reality behind it.

    For founder-led firms, that usually shows up as missed leads, messy handovers, and inconsistent output. For more regulated sectors, it can create trust friction before a proposal is even on the table.

    A sensible pre-launch checklist

    Before putting an AI assessment in front of serious buyers, I would want five things in place:

    • a tested fallback path for report generation
    • sanitised, structured output with a clear advisory disclaimer where needed
    • a defined CTA path into the services page or the contact page
    • privacy, retention, and access decisions written down
    • alerts and admin workflows that fail visibly rather than silently

    That will not make the launch flashy. It will make it usable.

    And in this category, usable beats flashy every time.

    If you are building an AI assessment, advisory funnel, or client-facing automation journey and want the surrounding controls designed properly, my services cover that mix of security leadership, IT operating model, and AI architecture. If you already have something live, get in touch and I can help you pressure-test it before it becomes a trust problem.

  • The M365 Audit You Can Run Before Calling a Consultant

    The M365 Audit You Can Run Before Calling a Consultant

    Last month I published a post about the M365 security baseline most SMEs skip — the default tenant configuration that leaves businesses exposed because no one with security expertise has reviewed it since the initial migration.

    That post explained what to check. This one gives you the tool to check it.

    I built an open-source M365 Audit Toolkit and put it on GitHub. It is a set of read-only PowerShell modules that connect to your tenant using your credentials, interrogate the configuration, and produce a structured report of what is misconfigured, missing, or ranging beyond what you would reasonably accept. It does not change anything. It does not write data. It reads, evaluates, and reports.

    And it is designed so that you can run it yourself — before you pick up the phone to a consultant.

    Why a Self-Service Audit

    Most UK SMEs operate in a state of confident uncertainty about their M365 tenant. The IT provider set it up. It works. Email flows. SharePoint files sync. Teams calls connect. Everything appears fine.

    But “fine” is not a security posture. It is an absence of visible failure.

    The problem is that the gaps in an M365 tenant are invisible until they are not. A compromised account exposes every SharePoint site the user can reach. A forgotten guest account with access to a shared folder persists indefinitely. A mailbox that has never been audited leaves no trail when something goes wrong. These gaps do not generate alerts. They do not appear in the admin console with a red warning icon. They sit quietly until a breach unmasks them, a subject access request forces you to find every piece of personal data in the tenant, or a client’s due diligence questionnaire asks questions you cannot answer.

    I built the toolkit because the cost of a structured audit should not be a barrier to knowing where you stand. You should not need to engage a consultant just to find out whether your conditional access policies are configured, your admin roles are clean, or your retention policies align with your data retention schedule.

    Run the audit first. Understand your gaps. Then decide whether you need help closing them.

    What the Toolkit Covers

    The M365 Audit Toolkit is modular. You can run the full audit or pick the modules relevant to your situation currently and build from there. At present it covers five areas.

    Security Baseline Assessment. Checks MFA enforcement across all users, verifies that legacy authentication protocols is blocked, reviews conditional access policy configuration, mailbox auditing status, and DLP label deployment. This maps directly to the seven controls most SMEs skip.

    Admin Role Hygiene. Identifies every global administrator in the tenant, flags accounts that should not hold that role, checks for service principal permissions that grant equivalent access, and reports on role-based access control adoption for other administrative functions. If your tenant has four, six, or eight global admins, this module will tell you exactly who they are and what they can do.

    Guest Access Audit. Enumerates every external guest account in the tenant, what they can access, when they were last active, and whether access reviews are configured. This is the module that finds the former contractor who still has access to your shared folders three years after their engagement ended — which is more common than you would think.

    SharePoint and OneDrive Governance. Reviews site-level sharing settings, detects externally shared content, checks for orphaned sites, and reports on storage allocation and versioning configuration. If you have ever wondered how much of your organisation’s data is sitting in personal OneDrive accounts that the business does not back up and cannot access, this module provides the answer.

    AI Readiness Evaluation. Assesses your tenant’s readiness to adopt Copilot and other AI services safely — reviewing sensitivity label coverage, data loss prevention configuration, and access controls that would govern what an AI service can surface to users. This is increasingly relevant as Microsoft bundles Copilot into enterprise and business tiers.

    How to Run It

    The toolkit uses interactive MFA authentication, meaning it connects as you and inherits your permissions. It does not require app registrations, client secrets, or service accounts. If you have global reader access to the tenant, you can run the audit.

    The general workflow:

    1. Clone the repository from GitHub and review the modules.
    2. Connect to your tenant using interactive browser-based MFA authentication.
    3. Select the modules relevant to your assessment — security baseline, guest access, governance, AI readiness, or all of them.
    4. Review the output as a structured summary showing what passed, what failed, and what needs attention.

    The entire process can be completed in under an hour for a typical SME tenant. The output is a point-in-time snapshot — it does not persist data, and it does not leave artefacts in your tenant beyond the sign-in logs generated by your own authentication.

    When to Call a Consultant

    Running the audit yourself is the right first step. But there are situations where the findings will tell you that it is time to bring in expertise.

    • The report is long and you do not know where to start. If every module returns significant findings, the remediation effort is material and benefits from prioritisation and sequencing — which is exactly what a structured engagement provides.
    • You lack the permissions or authority to make changes. If the audit reveals that conditional access policies need to be reconfigured but your IT provider controls the tenant, the value of the report is in telling you exactly what to ask for and why.
    • You are preparing for client due diligence, a cyber insurance application, or a regulatory review. The audit output gives you a factual baseline, but remediation may need to be documented, evidenced, and in some cases certified.
    • You are adopting Copilot or other AI services. The AI readiness module will tell you where your gaps are, but closing them — sensitivity labels, access controls, DLP policies — is a project that benefits from experience doing it.

    The point is not to avoid consultants. The point is to arrive at that conversation informed, with a clear picture of what needs to be done rather than starting from a blank page.

    The Checklist Before You Pick Up the Phone

    If you want to understand your M365 tenant before committing to an engagement, here is the minimum viable assessment:

    • [ ] Run the security baseline module and resolve any critical MFA or conditional access findings
    • [ ] Run the admin role module and reduce global administrators to two or three accounts
    • [ ] Run the guest access module and revoke access for accounts no longer needed
    • [ ] Review the SharePoint governance report for externally shared sites and orphaned content
    • [ ] Review the AI readiness output if Copilot adoption is on your roadmap

    If you can work through that list and resolve the findings, your tenant is in materially better shape. If you cannot, you now have a specific, evidenced brief for what needs to happen next.

    The M365 Audit Toolkit is available now on GitHub. It is free, read-only, and built for exactly this use case.

    If you run it and the findings warrant a conversation, the Security & Compliance Strategy service covers M365 tenant configuration as part of a broader engagement. Or get in touch with your report — I can tell you within a 30-minute call whether the gaps are ones you can close yourself or whether it is time to bring in support.

  • Weekly Development Roundup: Site Enhancements and Tooling Updates

    Small changes rarely look impressive in a release note. A heading level is corrected, a focus state becomes visible, a dependency is updated, or a handover note is finally written. None of those changes is likely to make a product announcement. Together, they determine whether a website remains usable, maintainable, and trustworthy.

    This week’s development work followed that pattern. The focus was not on adding a flashy feature. It was on reducing friction and making the public-facing site easier to understand and safer to maintain.

    What changed

    The work covered several connected areas.

    Brand and presentation

    The site’s logo and footer were brought into closer alignment with the current visual direction. Brand consistency is not only a design concern: repeated small inconsistencies make a site feel less carefully maintained, especially when visitors move between the homepage, service pages, and articles.

    Accessibility

    The accessibility work focused on practical details that affect real navigation:

    – heading structure was corrected so pages are easier to scan with assistive technology;
    – focus states were made visible for keyboard users;
    – colour choices for accent text were reviewed against contrast expectations; and
    – reduced-motion preferences were respected where animation or transition effects were present.

    These checks are easy to postpone because the page can still appear to work in a quick visual review. That is exactly why they need to be part of the normal delivery process rather than treated as a final polish task.

    Content and metadata

    Content fixes removed duplicate blog-title rendering and corrected placeholder metadata. A page can be technically functional while still giving search engines, social previews, or visitors the wrong information. Accurate titles, descriptions, and calls to action are part of the product surface.

    The same principle applies to promotional copy. If a feature is not live or supported, the page should not imply that it is. Clear limitations are more credible than ambitious wording that a visitor cannot verify.

    Dependency maintenance

    Dependency updates are another form of housekeeping that is easy to underestimate. Keeping a framework and its supporting packages within a supported range reduces the size of future upgrade jumps and makes security review more manageable.

    An automated update tool can identify candidates, but it cannot replace review. A sensible update process should still check the changelog, run the project’s tests or build, inspect the rendered result, and record any decision to defer a major change. “Updated” is not the same as “verified”.

    Documentation and handover

    The development work also included design notes and decision records. These documents are useful when the original context is no longer fresh, when another person needs to deploy a change, or when a later decision appears to contradict an earlier one.

    Documentation is not a substitute for a maintainable system. It is a way of making the system’s assumptions visible so that maintenance does not depend on one person remembering why something was done.

    What this says about ongoing maintenance

    The common thread is that quality comes from repeatable checks rather than occasional heroic clean-ups.

    A useful weekly maintenance pass can ask:

    1. Can a keyboard user move through the important journeys?
    2. Do headings, metadata, and calls to action describe the page accurately?
    3. Are dependencies inside an intentional support window?
    4. Does the build pass after updates?
    5. Could another operator deploy or recover the site from the written notes?
    6. Are unfinished experiments clearly separated from public claims?

    The answers do not need to be perfect every week. They do need to be visible. A small, recorded decision to defer an upgrade is safer than an unspoken assumption that somebody else is handling it.

    Looking ahead

    The next areas of work are the content workflow, further performance and accessibility checks, and a review of privacy-friendly analytics options. Each will be treated as a change to a live service rather than an isolated task: define the intended behaviour, test the change, check the user experience, and document the result.

    That approach is deliberately unglamorous. It is also how small improvements compound into a site that is easier to use, easier to support, and less likely to surprise its owner.

    If your website or internal digital service needs a similar review of accessibility, maintenance, and operating discipline, visit my services page or contact page.

  • Why Dependency Hygiene Becomes a Leadership Issue

    Why Dependency Hygiene Becomes a Leadership Issue

    A lot of teams still treat package upgrades and release housekeeping as purely technical chores.

    That made sense when the application estate was simpler, the change rate was slower, and a missed patch mostly meant a slightly awkward sprint later on. It makes much less sense once your organisation depends on AI services, automation workflows, customer-facing portals, and a growing set of third-party components that can change under your feet.

    This week’s GitHub activity was a useful reminder of that.

    Across several public repos, the work was not centred on a dramatic new feature. It was centred on the quieter jobs that keep live systems usable: upgrading a framework across multiple sites, tightening the way an agent platform distinguishes billing exhaustion from authentication failure, restoring an explicit safety guard with regression coverage, and shipping deployment handover notes alongside a website redesign.

    That mix matters because it points to a broader operating reality: dependency hygiene is no longer just a developer preference. It has become part of leadership discipline.

    The recent GitHub signals were small, but not trivial

    A few examples stood out.

    One public media/website repo recently went through a cluster of framework upgrades across multiple surfaces. On the face of it, those are dependency bumps. In practice, they are evidence of someone choosing not to let the front-end estate drift quietly into a harder future upgrade.

    Another public analytics/cost-tracking repo saw updates to numerical and coverage tooling. Again, this is the sort of work many leadership teams never see. Yet if cost reporting or analytics underpin operational decisions, stale numerical libraries and test tooling are not invisible engineering details. They affect how confidently the team can rely on output.

    In one public agent platform, a recent change separated billing exhaustion from a generic authentication failure, while another restored a missing enabled-guard and added regression tests. Those are strong examples of operational maturity. The first makes failure states easier to understand. The second makes it harder for a disabled service path to behave ambiguously.

    Then there was website delivery work in a public site repo: one change added a deployment handover document, and another shipped the full site build and deploy tooling. That is not just design delivery. It is an acknowledgement that shipping is only half the job if nobody else can support the result safely afterwards.

    Why senior leaders should care about this work

    If you are a founder, managing partner, COO, CIO, or board sponsor, none of this should be dismissed as back-room maintenance.

    Once AI, automation, or client-facing digital journeys are part of the operating model, dependency hygiene becomes a business issue for three reasons.

    1. Drift increases the cost of every later decision

    Technical drift rarely fails all at once. It accumulates quietly.

    A framework stays two major versions behind. A library update gets deferred because it is inconvenient. A brittle edge case stays undocumented because the team who understands it is still around. Nothing looks urgent until a new feature, security fix, or partner integration suddenly depends on clearing months of deferred maintenance.

    That is when the bill arrives.

    For SMEs and founder-led businesses, that usually shows up as delivery drag. For law firms and healthcare organisations, it can also create assurance problems because the stack behind a client-facing process becomes harder to explain. For PE-backed companies, it turns into diligence friction. If the technology estate looks under-maintained, every transformation claim starts to feel less believable.

    2. Poorly classified failures waste leadership attention

    One of the most useful repo changes this week was not a new feature at all. It was the decision in one public agent platform to separate billing exhaustion from a generic auth failure.

    That matters because leaders make bad decisions when the system reports the wrong category of problem.

    If a model provider has hit a spend limit, that is a budget, quota, or supplier-management conversation. If the credentials are wrong, that is a configuration or secrets-management conversation. If both are lumped together as “auth failed”, the team loses time, the escalation path gets muddled, and confidence in the monitoring starts to erode.

    At scale, this is one of the easiest ways for senior people to get dragged into operational noise that should have been designed out earlier.

    3. Handover quality is part of risk control

    I think many organisations still underestimate how much risk sits in the gap between “it works” and “someone else can run it”.

    That is why the deployment handover work in dh-electrical-uk-website is commercially important. When a redesign lands with deploy tooling and supporting handover notes, the delivery is stronger than a visually successful launch on its own. It means the service is more likely to survive staff changes, supplier changes, holiday cover, and the inevitable moment when something needs to be updated under time pressure.

    In practical terms, handover is where a lot of hidden fragility gets exposed:

    • which versions are actually supported
    • which environment assumptions are undocumented
    • which steps still live in one person’s head
    • which recovery actions have never been written down

    If that sounds familiar, the issue is not documentation style. It is operating-model debt.

    What good leadership looks like here

    The answer is not for non-technical leaders to micromanage every package bump.

    The answer is to treat upgrade hygiene, failure clarity, and supportability as visible management concerns. In practice, I would want four things.

    Keep an explicit tolerance for drift

    Not every dependency needs to be latest immediately. But the organisation should know what level of lag is acceptable, where major-version changes are being deferred deliberately, and who owns the call when a deferral becomes risky.

    Insist on failure signals that guide action

    If an automation platform cannot distinguish billing, auth, config, runtime, and policy problems cleanly, the team will spend too much time diagnosing symptoms and not enough time fixing causes.

    Ask for handover evidence before calling a project done

    If a supplier, internal team, or blended delivery setup cannot show you the deploy path, the support notes, and the recovery assumptions, you do not yet have a finished operational asset.

    Connect maintenance discipline to commercial credibility

    Clients, investors, regulators, and acquirers do not usually ask whether numpy or Astro was bumped on Tuesday. They do care whether your digital services are dependable, whether your reporting is trustworthy enough to guide decisions, and whether your systems can be maintained without heroics.

    That is the commercial expression of dependency hygiene.

    A simple question worth asking this quarter

    If your organisation relies on AI, automation, or a modern web estate, ask one blunt question:

    Which live services would become awkward or risky to change if the current operator disappeared for two weeks?

    The answer usually reveals more than a tooling audit alone.

    It shows where drift has been tolerated too long, where failure messages are too vague to manage well, and where handover has been assumed rather than designed. Those are exactly the places where fractional leadership earns its keep, because the problem is rarely just a code problem. It sits across governance, service design, supplier control, and risk appetite.

    If that question exposes uncomfortable gaps, that is useful. It gives you a practical starting point.

    If you want help turning that picture into a calmer operating model, my services cover the overlap between security leadership, IT direction, and AI architecture. Or get in touch if you want a senior review of where maintenance debt, weak handover, or ambiguous failure handling is making change harder than it should be.

  • Before You Launch an AI Assessment, Fix the Operating Model

    Before You Launch an AI Assessment, Fix the Operating Model

    A lot of AI advisory offers now start with an assessment.

    The problem is that many of these offers are still being built like marketing assets rather than client-facing systems.

    If the output influences buying decisions, budget allocation, compliance posture, or board discussion, the operating model behind it matters as much as the prompt or the front end. Recent GitHub work around an AI consultancy assessment build, plus reliability work in adjacent automation repos, makes that obvious.

    The recent signals are not about polish alone

    One assessment build in particular stood out this week.

    The visible activity was not just about shipping a shiny MVP. It included concrete follow-up work such as:

    • provider fallback handling for report generation
    • safer rendering of generated reports and clearer visitor handoff paths
    • lead email notification flows
    • privacy, GDPR, and security controls
    • a protected admin dashboard for internal review

    That list is commercially useful because it shows the build moving away from “can we generate an AI report?” and toward the harder question: “can we run this as a client-facing service?”

    The same instinct showed up elsewhere. In one public agent platform, recent changes added approval requirements around sensitive gateway replacement paths and tightened behaviour when an API server is explicitly disabled. In another internal management context, recent work also focused on surfacing failed or missing configuration steps as real errors rather than false-green output.

    An AI assessment is a service, not a content asset

    This is the point many firms miss.

    An AI assessment may arrive through a landing page, but from the user’s point of view it behaves like a service. It collects inputs. It processes them. It generates output that may shape strategy. It creates follow-up work for your team. It may retain commercially sensitive information. It may trigger email workflows or lead handling.

    That means buyers, especially in law firms, healthcare organisations, PE-backed businesses, and regulated SMEs, will judge it on more than whether the wording feels intelligent.

    They will care about questions such as:

    • What happens if the model fails halfway through a report?
    • Where does the submitted information go?
    • Who can see the results internally?
    • Will someone follow up while the lead is still warm?

    Those are operating-model questions. If they are answered late, the launch becomes fragile. If they are answered early, the assessment becomes a serious commercial asset.

    The four controls I would design before launch

    When I look at the recent issue set, I see four controls that should exist before any AI assessment is treated as production-grade.

    1. Fallbacks for report generation

    Provider fallback handling is one of the first giveaways that the team is thinking properly.

    If your report workflow depends on a single provider, a temporary outage or degraded model response can turn a promising user journey into a dead end. A fallback model path does not need to be elaborate on day one, but it does need to exist. You should know:

    • which provider is primary
    • which fallback path is acceptable
    • how quality is checked before the output is shown
    • what the user sees if both paths fail

    2. Safe rendering and an explicit handoff path

    Safe rendering and an explicit handoff path may sound like front-end housekeeping, but they are more important than that.

    AI-generated output often carries awkward structure, inconsistent formatting, and the occasional sentence that reads far more confidently than the evidence supports. Treat output rendering as a control surface: sanitize it, structure it, keep the language disciplined, then give the reader a clear next step.

    A practical CTA is part of the safety model here, not just the conversion model. If the output is intended to open a commercial conversation rather than substitute for expert judgement, the interface should say so and point naturally to the services page or the contact page.

    3. Privacy, GDPR, and role-based access

    For UK buyers, especially in legal and healthcare environments, privacy, GDPR, and role-based access are where a build starts becoming credible.

    An assessment tool often collects exactly the sort of operational detail that organisations do not want sprayed across logs, inboxes, and loosely protected admin views. Decide early:

    • what data is stored
    • what is redacted or minimised
    • how long submissions are retained
    • which internal roles can access raw answers
    • what the lawful basis and privacy notice look like

    A protected admin dashboard belongs in the same conversation. Internal convenience is not a good enough reason for weak access control.

    4. Truthful monitoring and approvals around the edges

    The adjacent repo activity matters here because it reinforces a broader discipline.

    If a nightly check can go false-green, or a gateway action can happen without the right approval, your delivery stack is already telling you something about risk appetite. Public agent-platform fixes and internal management work both point to the same lesson: the system around the assessment needs honest signals and controlled change paths.

    For a buyer-facing AI assessment, keep one rule in mind: do not automate yourself into ambiguity.

    If emails fail, surface it. If a fallback is used, log it. If an admin action changes routing or content, require the right level of review. If an integration is disabled, behave safely and obviously rather than trying to muddle through.

    That is how you keep confidence high without pretending the system is infallible.

    Where this lands commercially

    This is not only a product design issue. It affects how the market reads your firm.

    A well-run assessment signals seniority. It tells a prospect that you understand not just AI tooling, but governance, service design, delivery risk, and follow-through. A weakly controlled assessment suggests the front-end story is outrunning the operating reality behind it.

    For founder-led firms, that usually shows up as missed leads, messy handovers, and inconsistent output. For more regulated sectors, it can create trust friction before a proposal is even on the table.

    A sensible pre-launch checklist

    Before putting an AI assessment in front of serious buyers, I would want five things in place:

    • a tested fallback path for report generation
    • sanitised, structured output with a clear advisory disclaimer where needed
    • a defined CTA path into the services page or the contact page
    • privacy, retention, and access decisions written down
    • alerts and admin workflows that fail visibly rather than silently

    That will not make the launch flashy. It will make it usable.

    And in this category, usable beats flashy every time.

    If you are building an AI assessment, advisory funnel, or client-facing automation journey and want the surrounding controls designed properly, my services cover that mix of security leadership, IT operating model, and AI architecture. If you already have something live, get in touch and I can help you pressure-test it before it becomes a trust problem.

  • The M365 Security Baseline Most SMEs Skip

    The M365 Security Baseline Most SMEs Skip

    If your business runs on Microsoft 365 — and in the UK, that covers the vast majority of SMEs, law firms, and healthcare practices — there is a reasonable chance your tenant is less secure than you think.

    Not because Microsoft has failed. Not because your IT provider has been negligent. But because the default configuration of an M365 tenant is designed to get you up and running, not to protect a regulated business handling sensitive client data.

    Most organisations I work with assume that because Microsoft provides the platform, Microsoft secures it. That assumption is wrong, and it is the single most common gap I find when reviewing an SME’s security posture.

    The Shared Responsibility Model, Explained Simply

    Microsoft operates what is called a shared responsibility model. Microsoft secures the platform: the physical data centres, the hypervisor, the network infrastructure, the availability of the service. That part is genuinely well handled.

    What Microsoft does not do is secure your tenant. Your tenant is your configuration: who can log in, from where, with what level of verification. What happens to data when it leaves your mailbox. Who has access to your SharePoint sites. Whether a former contractor’s guest account is still active three years after they left.

    These are your decisions. Microsoft gives you the controls. It is up to you to turn them on and configure them correctly.

    The problem is that most SMEs never have this conversation. The tenant was set up when the business migrated to M365, the defaults were accepted, and no one with security expertise has reviewed the configuration since.

    The Seven Controls Most SMEs Skip

    When I conduct a baseline M365 security review, the same gaps appear with striking consistency. Here are the seven controls that are most commonly missing or misconfigured.

    1. MFA enforcement for all users. Multi-factor authentication is the single most effective control against credential-based attacks. It is also the one most likely to be partially deployed. I regularly find tenants where MFA is “enabled” but not “enforced” — a distinction that means users can still bypass it. Every account should have MFA enforced, without exception.

    2. Conditional access policies. MFA alone is not enough if it can be triggered from any device, on any network. Conditional access lets you require compliant devices, block legacy authentication, restrict access by location, and require step-up authentication for sensitive applications. Most SMEs I review have no conditional access policies configured at all.

    3. Mailbox auditing. M365 includes mailbox auditing as a standard feature, but it is not always enabled by default on older tenants. Without it, you have no record of who accessed a mailbox, what they did, and when. If a compromised account is used to exfiltrate email, you will not know. For law firms and healthcare organisations, this is a basic compliance requirement.

    4. DLP labels and policies. Data loss prevention lets you define sensitivity labels and apply policies that prevent data from leaving the organisation — for example, detecting when someone emails a document containing a National Insurance number or bank account detail to an external address. Most SMEs have no DLP policies. Those that do often run them in “test mode” that generates alerts but takes no action.

    5. Guest access controls. By default, M365 allows users to invite external guests to SharePoint sites, Teams channels, and shared folders. Without controls, a member of staff can share a folder containing sensitive client documents with an external address, and that access persists until someone manually revokes it. Guest access should be restricted by domain and subject to regular review.

    6. Retention policies. Without retention policies, everything stays in the tenant indefinitely — including data the business no longer needs, data it is not legally permitted to retain, and data that would be damaging in a breach or subject access request. Retention policies should reflect the organisation’s actual data retention schedule.

    7. Admin role hygiene. Global administrator grants full access to every service and every piece of data in the tenant. Most SMEs I review have between four and eight global administrators. The correct number is two or three, used exclusively for administration. Every additional global admin is an additional high-value target. Role-based access control should be used for everything else.

    Why This Matters: The Blast Radius of One Compromised Account

    The business risk here is not theoretical. A single compromised M365 account — obtained through phishing, credential stuffing, or a brute-force attack against an account without MFA — gives an attacker access to that user’s email, their OneDrive files, the SharePoint sites they can reach, the Teams channels they belong to, and every third-party application connected to the tenant.

    For a law firm, that could mean access to client matter files, privileged correspondence, and case strategy documents. For a healthcare practice, it could mean patient records and clinical communications. For any business, it could mean the ability to send convincing phishing emails from a trusted internal address to every contact in the organisation.

    The attacker does not need to breach your firewall. They do not need to exploit a vulnerability in your infrastructure. They need one set of credentials, and the default M365 configuration hands them the keys to everything.

    The Baseline Checklist

    If you want to assess where your organisation stands, here is a practical checklist. You can work through this with your IT team or your IT provider. Every item should be a yes or a concrete plan — not a “we think so” or “it should be on”.

    • [ ] MFA is enforced for every user account, without exceptions
    • [ ] Legacy authentication protocols are blocked via conditional access
    • [ ] Conditional access policies restrict access by device compliance and location
    • [ ] Mailbox auditing is enabled and logs are retained for at least 90 days
    • [ ] DLP policies are configured for sensitive data types and set to enforce, not just test
    • [ ] Guest access is restricted by domain and subject to regular access reviews
    • [ ] Retention policies are configured and aligned with the organisation’s data retention schedule
    • [ ] Global administrator roles are limited to two or three accounts, used only for administration
    • [ ] Role-based access control is used for all other administrative functions
    • [ ] A regular access review process is in place for both internal and external users

    If you can tick every box, your baseline is in good shape. If you cannot, you have a clear picture of where to start.

    Where to Start

    You do not need to fix everything at once. The highest-impact changes — MFA enforcement, blocking legacy authentication, and reducing global administrator count — can be implemented in a single afternoon and will meaningfully reduce your exposure.

    The rest can be prioritised based on your risk profile. A law firm handling privileged client data will prioritise DLP and mailbox auditing differently than a professional services firm with a smaller client base. The point is to make deliberate decisions about your configuration, not to accept the defaults and hope they are enough.

    If your organisation runs on M365 and you are not confident that your tenant is configured to a standard that would withstand scrutiny — from a regulator, a client, or an attacker — a structured security baseline review is the right first step. The Security & Compliance Strategy service covers M365 tenant configuration as part of the broader risk framework. Or get in touch for a 30-minute conversation about where your organisation stands.

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