The Challenges of AI in the UK Public Sector Heading into 2027

Imagine a council officer using AI to summarise a resident’s case. The summary reads well. Checking it means reopening the original documents, and the next appointment starts in five minutes.

How much time has the tool actually saved? Who takes responsibility if it missed something important?

For public sector leaders planning for 2027, the biggest AI challenge is making room for careful judgement inside services already under pressure.

At DigiGov, our team heard speakers acknowledge staff anxiety about AI and job losses. Conversations also returned to the quality of data feeding these systems and the need for people to assess their outputs.

Those concerns echo wider findings. In July 2026, the government’s Local AI team reported feedback from 60 councils: guidance was too abstract for everyday decisions, while skills to procure and govern AI remained a problem.

Infographic titled 'Public Sector AI: Five Challenges for 2027', outlining key challenges including staff confidence, security of AI-generated code, AI in existing software, time to check outputs, and clear accountability.
AI Challenges for the Public Sector

Human oversight needs time and authority

A named reviewer cannot provide meaningful oversight without access to the original information or permission to challenge the result.

Take the council summary. A useful review process would identify facts requiring verification, such as dates or eligibility details. The officer should be able to trace those facts to their source.

For each workflow, agree:

  • Which outputs need checking before anyone acts on them?
  • Who can pause the process when something looks wrong?
  • What happens when that person is unavailable?
  • How will the service continue if the AI tool is switched off?

Then rehearse those steps with a deliberately flawed output. 

A research by the Ada Lovelace Institute in February 2026 found an AI-generated summary that incorrectly suggested a client had expressed suicidal thoughts. A social worker caught the error before it entered the case note. Had it gone unchecked, it could have influenced later decisions about that person’s care.

Use that kind of mistake to test your own safeguards. Give staff a fictional case summary containing a consequential error and ask them to check it against the source material under realistic time pressure. 

Can they catch and correct it before anyone acts? If checking takes longer than the workflow allows, build that time into the service before expanding AI use.

An illustration showing a hand pulling back a paper revealing an AI-generated care summary, highlighting the phrase 'The client expressed suicidal thoughts.' with a warning that the statement was invented by AI. The image emphasizes the importance of verifying AI summaries before they are officially recorded.
AI Can Still Make Critical Mistakes

Supplier AI features need their own review

One concern raised in our DigiGov discussions was AI entering departments through software they already use. An existing supplier relationship can make a new feature feel familiar before anyone has assessed what it changes.

A meeting assistant, for example, could introduce new questions about where recordings go and how long they remain there.

Ask suppliers to disclose new AI features before activation. Establish what information each feature can access and whether the organisation can disable it. Name a service owner responsible for approving changes, with security and procurement support.

For AI agents that can take actions, define the permissions explicitly. Drafting a response and sending it to a resident requires different controls.

AI-generated software still needs security checks

The same responsibility extends to development. Developers we met at DigiGov were using or exploring AI coding assistants, raising a practical question about how their suggestions are checked.

Government guidance updated in August recommends additional vulnerability scanning and checking dependencies introduced by coding assistants against trusted sources.

An AI-generated function may work while relying on a vulnerable open-source package. Review the code and scan its dependencies before release. Keep monitoring afterwards, because a component’s risk can change when a vulnerability is disclosed.

Put checks where people already work

For 2027 planning, start with one live workflow. Assign an owner and test the review process before expanding it. Budget for the checking work.

Meterian supports the software security part of that approach. HEIDI helps developers identify vulnerable dependencies and suggested fixes while coding. Meterian’s wider platform provides continuous dependency monitoring and licence risk checks within development pipelines. 

Talk to Meterian about checking the open-source components in your public services, including software built with AI assistance.

The Challenges of AI in the UK Public Sector Heading into 2027

How Quickly Can You Trace Vulnerable Code Across Public Services?

In benchmark testing, OpenAI’s latest model, GPT -6 Astra, was able to find unknown zero-day vulnerabilities and exploit them in real-time. Although this capability is being sandboxed and restricted for now, this finding should worry everyone, especially those responsible for public services. 

The security threat landscape is moving to a mass risk of zero-days, yet the public sector still struggles to respond to known vulnerabilities, especially third-party flaws that take an average of 358 days to fix.

To see where the response can stall, let’s follow a vulnerability alert through a public service and examine what it takes to get the affected code fixed. 

How fast is fast enough?

Imagine a council security team receiving an alert about a flaw in software used to process uploaded documents. A scan finds the affected component in several projects, including one that appears to support the housing repairs portal.

The service director wants to know whether residents can keep using it. The team needs to check which version is running and whether the upload function exposes the flaw. An external supplier maintains the application.

The scan has taken minutes. Answering the director could occupy much of the morning.

In Veracode’s 2026 State of Software Security research, third-party flaws took an average of 358 days to fix and accounted for 66% of critical security debt in its dataset.

Public-service leaders can examine the delay within their own organisations, starting with the work required to turn a component finding into an agreed plan for the affected service.

Graphic showing statistics on third-party software flaws: '358 days' as the average time to fix these issues, alongside '66%' indicating the percentage of critical security debt from third-party components.

The search needs to reach below the application

A service register might identify the repairs portal and its supplier. It is unlikely to tell the security team which open-source library handles a resident’s uploaded photograph.

That detail belongs in the software’s component inventory. A software bill of materials, or SBOM, records the components in a build, including their versions. For this investigation, the inventory must cover dependencies brought in by other packages, known as transitive dependencies.

The distinction matters when the development team says it has never selected the affected library. Another component may have introduced it.

Search results should show that dependency path. It helps the engineer determine which package needs updating, especially when the vulnerable library cannot be changed independently.

Then check the inventory’s scope and date. Confirm that supplier-managed software is included in the search. If it is excluded, the team needs evidence from the supplier before ruling the service out.

Graphic depicting an iceberg with 'What's Beneath Your Public Service?' text. The top shows 'Public Service', followed by 'Application', and at the bottom, 'Vulnerable Dependency' indicated with an exclamation mark.

Follow the finding all the way to production

Suppose the supplier confirms that the library appears in the portal’s codebase. Its latest development build already contains a corrected version.

The council still needs to establish what residents are using today.

Link the component finding to a specific build, then check where that build was deployed. The evidence should identify the live service and any running instances that still contain the affected version.

This connection takes preparation. Repository names rarely explain a service’s purpose to someone outside its delivery team. Maintain the relationship between each project and the service it supports as part of normal delivery. Changes of supplier or platform are good moments to check that the record still holds.

Once the team locates the affected release, it can assess exposure. Some vulnerabilities require particular settings or a reachable function before an attacker can exploit them. Record the relevant configuration and the reasoning behind the assessment.

That gives the service director something useful to act on. If the upload function is exposed, restricting it may be appropriate while a fix is prepared. The decision must account for how residents will submit repair requests during that restriction.

“We’re investigating” needs a next step

A supplier’s acknowledgment can arrive quickly while useful answers take longer.

In our scenario, the council needs a response tied to its deployed release. The supplier should explain whether that release is affected and identify the proposed remedy. Agree when the next substantive update will arrive.

Inside the council, assign someone to coordinate the remediation. That person needs a working technical contact at the supplier and a route for escalating delays. The accountable service owner must also be available to approve operational decisions.

Ownership should be established before the alert arrives. Test the escalation route during routine supplier reviews. A contract reference is little help if the named contact has moved on or cannot reach the engineers responsible.

Where several public services share the same component, assess their exposure individually. Prioritisation should reflect evidence of exploitation and the consequences for each service. A common package does not mean every deployment needs the same response.

The fix has to survive contact with the service

A suggested replacement version gives engineers a starting point. It still needs testing against the application.

An upgrade can change behaviour elsewhere in the dependency chain. For the repairs portal, regression testing should include the document-upload workflow residents rely on. The team also needs a recovery plan if deployment causes problems.

Keep the finding open until the evidence shows that the corrected build has reached the affected instances. Recheck the deployed software and retain the result with the remediation record.

If an update must wait, document the reason and set a review date. Any temporary restriction needs someone responsible for checking that it remains effective. Otherwise, the next person handling the service inherits an unresolved decision with little explanation.

Find out where your own morning goes

Choose one important service and rehearse this investigation using a component it actually contains. Time how long the team takes to produce:

  • A deployment record: the affected version linked to the live service.
  • An exposure decision: the evidence supporting the assessment.
  • A named lead: someone able to coordinate remediation and escalate delays.
  • A tested route to closure: the proposed update and the checks required after deployment.
Infographic outlining the steps to getting a fix live: 1. Locate the affected service, 2. Assess if the flaw is exploitable, 3. Assign responsibility for the fix, 4. Verify if the fix is live and working.

Record where the investigation stalls. Waiting for supplier evidence requires a different remedy from finding an ownerless repository. Repeat the exercise once the team has addressed the delay.

Meterian can support the component investigation by identifying dependencies across projects and monitoring published vulnerabilities. Its reports provide remediation guidance, with integration into existing delivery pipelines. Teams can connect those findings to their service records to complete the investigation.

We’ll be discussing that work at DigiGov Expo, 23–24 September 2026, at ExCeL London. Meet Meterian to explore how quickly your team could trace an affected component through to a public service and put someone in charge of the fix.

How Quickly Can You Trace Vulnerable Code Across Public Services?

Manufacturing Software Supply Chains Need the Same Scrutiny as Physical Suppliers

Manufacturers know how to qualify a physical supplier. Parts are traced to batches and tested before production. When a defect appears, there is a recall path.

Software often enters through a less visible route. A developer adds a package or a build tool pulls an action. The component can then reach a factory system without appearing in a traditional supplier register. This gap can completely shatter production resilience.

The factory depends on code it did not write

IBM’s 2026 X-Force Threat Intelligence Index found that manufacturing accounted for 27.7% of the incidents it observed in 2025. It was the most targeted industry for the fifth consecutive year.

The sector has an unusually broad attack surface. Corporate applications connect with operational technology, while software also runs inside products leaving the factory. One vulnerable component can therefore sit across systems with very different patching windows.

An illustration of a factory and a large open box revealing interconnected software dependency boxes, highlighting an 'Approved vendor' tag and a 'Hidden dependency' note.

This is where physical and software supply chains begin to resemble each other. Both contain lower-tier suppliers that the organisation may never deal with directly. In software, those hidden tiers are often transitive dependencies pulled in by another package.

A five-year-old flaw is still generating millions of detections

Recent manufacturing data shows how long software risk can persist.

SonicWall recorded 474 million intrusion-prevention events on manufacturing networks in the first half of 2026. Its sensors also registered 13.8 million Log4j2 detection events, more than four years after Log4Shell was disclosed and fixed versions became available.

These are detection events rather than confirmed breaches. They still show that old dependency risk remains active inside manufacturing environments long after the wider industry has moved on.

A patch can exist while an organisation struggles to find every affected system. The problem becomes harder when the same library appears inside factory applications, engineering tools and connected products maintained by different teams.

Illustration showing 13.8 million Log4j2 detections on manufacturing networks from January to June 2026, with a calendar for 2021 highlighting the Log4Shell disclosure.

The Trivy incident changed the trust question

The March 2026 compromise of Trivy showed that a trusted tool can become part of the attack path.

According to Aqua Security’s GitHub advisory, an attacker used compromised credentials to publish malicious releases of the open-source vulnerability scanner. The attacker also rewrote nearly every version tag in the Trivy GitHub Action so that affected build pipelines executed credential-stealing malware.

Teams used Trivy because they wanted to find vulnerabilities. During the exposure window, the scanner itself carried the risk.

The lesson is broader than one project. Supplier approval and a clean scan provide evidence at a point in time. Manufacturing teams also need to know which version entered each build and whether the source changed afterwards.

A software outage can reach thousands of suppliers

The 2025 Jaguar Land Rover cyber incident showed how quickly digital disruption can become a physical supply-chain problem.

The Cyber Monitoring Centre estimated a £1.9 billion impact on the UK and said more than 5,000 organisations were affected. Most of the modelled loss came from reduced manufacturing output at JLR and its suppliers.

The available public evidence does not identify an open-source dependency as the cause. The incident is still relevant because it shows the scale of exposure once software disruption stops production. A digital failure at one manufacturer can reach component suppliers and dealerships without directly compromising their systems.

Procurement cannot see a transitive dependency

Traditional supplier governance records the company providing a platform or industrial system. It rarely shows every open-source package inside that product.

Engineering teams may have part of the answer in manifests and lockfiles. Security may have separate scan results. Operations knows which system is critical. When these records are disconnected, the organisation cannot move quickly from a vulnerability notice to an affected production service.

Current dependency evidence closes that gap. A release-linked component inventory shows what was deployed. Continuous monitoring keeps the inventory useful when new vulnerabilities or malicious packages appear.

Make the evidence useful while code is changing

Dependency checks are most useful when developers can act on them. Waiting for a final pipeline gate can turn a small version change into a larger remediation task.

Developer-side checks can flag a vulnerable package as a manifest changes and point towards a safe version. The pipeline can then verify the same policy before release. This gives engineering and security teams a shared record without asking developers to leave their normal workflow.

A sensible starting point is one critical production service. Connect its component inventory to each release and test how quickly the team can locate a newly disclosed vulnerability. The result will show whether the software supply-chain map works under pressure.

Manufacturers already understand that supplier evidence must stay current. The same discipline now needs to reach the code running the factory.

Manufacturing Software Supply Chains Need the Same Scrutiny as Physical Suppliers