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Code for America highlights challenges in measuring AI use in public services in the US states

According to Code for America, AI is reshaping how public services are delivered across the United States, yet adoption remains uneven and difficult to measure. They added that state governments are rapidly embracing AI through low-risk pilot programmes while still lacking clear frameworks to evaluate impact.

The report describes AI adoption as following a staged progression beginning with readiness, where leadership structures, workforce skills and infrastructure are developed.

Piloting then introduces experimentation through sandboxes and limited deployments, while implementation embeds AI into operational systems such as fraud detection, document automation, research support and citizen-facing chat assistants.

The report also notes that despite growing experimentation, most US states have not yet transitioned into fully operational and measurable systems.

Leading states, including Utah, New Jersey, Pennsylvania, North Carolina, Maryland, Texas and Vermont, are advancing institutional capabilities required to govern AI as a long-term public asset. Others, such as West Virginia, Wyoming, Nebraska, Alaska, Florida and Kansas, remain at earlier stages of readiness and adoption.

The report identifies measuring outcomes as a key challenge. It states that while AI promises efficiency gains and cost reductions, short-term deployment often increases workload for public employees before benefits materialise.

It adds that evaluation frameworks remain underdeveloped, leaving governments with strong governance structures but limited visibility into real performance improvements.

According to Amanda Renteria, CEO of Code for America, the opportunity extends beyond adoption alone, as governments must shape AI in ways that are human-centred and grounded in measurable public outcomes.

The report suggests that states that succeed in aligning technology with real community impact will move beyond experimentation and define the future of public service in the AI era.

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UK’s NCSC warns AI could expose software vulnerabilities at scale

The NCSC says that AI is reshaping cybersecurity by exposing vulnerabilities across software ecosystems.
The National Cyber Security Centre (NCSC) warns that organisations must prepare for a large-scale patch wave. AI enables faster identification and exploitation of weaknesses than traditional defences can handle.

Technical debt, built through years of prioritising short-term efficiency instead of long-term resilience, is now being exposed at scale.

The NCSC notes that AI capabilities enable attackers to identify weaknesses faster and more comprehensively, creating pressure on organisations to respond with rapid and coordinated patching strategies across entire technology environments.

The recommended approach by NCSC prioritises internet-facing systems and external attack surfaces, followed by internal infrastructure and critical security assets.

Automated updates and hot patching are encouraged where available, while organisations lacking such capabilities must adopt scalable and risk-based update processes. Legacy systems without support present a particular risk, requiring replacement instead of reliance on patching alone.

NCSC adds that beyond software updates, the challenge reflects a deeper structural issue within digital ecosystems. Stronger cyber resilience depends on reducing systemic vulnerabilities through secure design practices, improved monitoring and supply chain readiness.

They also said that organisations that fail to prepare for continuous, large-scale patching cycles risk increased exposure as AI continues to reshape the cybersecurity landscape.

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US military expands AI deployment across classified networks

The US Department of Defence has announced agreements with leading technology firms to deploy advanced AI capabilities across classified military networks. The initiative forms part of a broader effort to position the United States as a more AI-enabled military power.

Companies including OpenAI, Google, Microsoft, Amazon Web Services, NVIDIA, and SpaceX are reported to be involved in supporting deployment within high-security Impact Level 6 and 7 environments. The integration is intended to improve data synthesis, situational awareness, and operational decision-making across defence systems.

The department’s internal platform, GenAI.mil, is also being presented as a central part of this push, with senior officials describing it as a way to put advanced AI tools into the hands of personnel across the department and across different classification levels.

Officials have emphasised that maintaining access to a range of AI providers is important to avoid vendor lock-in and preserve long-term flexibility. In that sense, the move reflects a wider attempt to strengthen national security through advanced technology while keeping the military AI stack diversified rather than dependent on a single company or model family. However, this is an inference based on the reported Pentagon framing of the agreements.

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Victorian officials outline approach to managing AI risks in public sector

Ian Pham at the Victorian Managed Insurance Authority (VMIA) outlined approaches to managing AI adoption during the PSN Victorian Government Cyber Security Showcase. Organisations face the challenge of adopting AI while maintaining effective risk management as these systems become more embedded in government operations.

Cybersecurity teams have traditionally operated with a risk-averse approach focused on minimising threats. Such an approach can slow innovation when applied to AI systems used in public sector environments.

A shift towards managing risk in line with organisational objectives is presented as necessary. This includes prioritising relevant risks and moving from reactive responses towards supporting decision-making processes.

AI adoption involves secure environments for experimentation with defined guardrails, including synthetic or non-sensitive data, monitoring mechanisms, usage conditions, and identity and access controls. Exposure can then be increased gradually, supported by governance and continuous reassessment.

Risks linked to AI systems include data leakage, privacy concerns, unauthorised use, and data quality issues. These risks are described as requiring visibility and management, alongside organisational awareness and engagement to support confidence in AI use.

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AI model raises security risks, prompting release concerns, reports say

Anthropic is reported to have declined to release its latest AI model, Mythos, citing potential risks to global cybersecurity. The system is reported to be capable of identifying vulnerabilities across major operating systems and web browsers, raising concerns about possible misuse.

Reports indicate that the company is investigating claims that unauthorised actors may have accessed the model. A reported breach has intensified debate about whether technology firms can maintain control over increasingly powerful AI systems as development accelerates.

The Mythos model is described as part of a new class of AI tools capable of analysing complex digital environments and identifying weaknesses at scale. Such capabilities could support cybersecurity efforts, but may also present risks if exploited by malicious actors.

The case has contributed to discussions within the technology sector about balancing innovation with efforts to manage potential risks to digital infrastructure.

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Singapore’s HTX signs agreements to advance public safety technologies

The Home Team Science and Technology Agency has signed 10 agreements with partners across government, industry and academia to advance public safety technologies. The announcement was made at MTX 2026.

The partnerships focus on areas including AI, space technology and cybersecurity, aiming to accelerate development of next-generation capabilities for public safety operations.

Several agreements involve industry collaboration to apply commercial innovations, while others expand research links with academic institutions to deepen expertise in areas such as forensics and autonomous systems.

HTX said the partnerships will strengthen collaboration, innovation and knowledge sharing across the public safety ecosystem. The agreements were announced at an event in Singapore.

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Study examines trust and fraud prevention in AI-enabled banking in Bangladesh

A new non-peer-reviewed preprint examines how AI is shaping e-banking in Bangladesh, focusing on consumer decision-making, ethical trust, and fraud prevention.

The paper links AI adoption in digital banking to customer experience, risk management, process automation, financial inclusion and regulatory compliance, arguing that these factors are increasingly important as Bangladesh’s financial sector becomes more digital.

A study that uses a narrative literature review of recent research from 2024 and 2025 and builds its conceptual model on the UTAUT2 framework, which is commonly used to explain technology adoption.

The authors extend the model by adding ethical trust and fraud prevention as mediating mechanisms, arguing that consumers are more likely to use AI-enabled banking services when they see them as useful, secure, transparent and fair.

Ethical trust is treated as a central part of adoption. The paper identifies transparency, algorithmic fairness, data privacy, reliability, accountability and digital inclusion as key factors shaping how users respond to AI in banking.

It also notes that explainable AI tools and localised interfaces, including Bengali-language systems, could help reduce uncertainty for users with lower digital literacy.

Fraud prevention is presented as a critical enabler of consumer confidence. The authors point to real-time monitoring, anomaly detection, secure authentication, biometric e-KYC and explainable fraud alerts as tools that can reduce perceived risk.

Additionally, they argue that AI systems should not only detect fraud effectively, but also explain decisions clearly enough for users to trust them.

The paper also highlights Bangladesh-specific issues, including Islamic banking, Shariah-compliant AI models, rural and urban digital access gaps, and the need for inclusive design. However, the study remains conceptual and has not yet been peer reviewed.

The authors recommend future empirical research with Bangladeshi banking users to test the model across income levels, regions, generations and gender groups.

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UK NCSC publishes framework on adversarial attacks against AI systems

The UK’s National Cyber Security Centre has published a paper on adversarial attacks against machine learning and AI, setting out a framework for understanding attacks that target the operation of ML models. The paper introduces a common language intended to support awareness, threat modelling, and collaboration on AI security.

The NCSC says ML systems present a larger attack surface than traditional software because of rapid development cycles, unique architectures, large model sizes, and the widespread use of open-source components. It distinguishes adversarial machine learning attacks from broader cyberattacks by focusing on those that exploit vulnerabilities specific to the architecture, training, or operation of ML models.

The paper defines seven attack classes:

model characterisation
model inversion
training data poisoning
malicious model training
model input manipulation
model artifact manipulation
model hardware attacks

It says these attacks can occur across development, training, and deployment, and may target both hardware and software components.

The NCSC also maps those attack classes against eight potential goals of a malicious actor, including reconnaissance, degrading performance, wasting resources, embedding hidden behaviours, evading detection, extracting data, and gaining wider system access. The table on pages 11-12 links each class to one or more of those goals.

The paper argues that standard cybersecurity controls remain foundational, but says ML-specific weaknesses often require dedicated mitigations that are not yet mature or widely deployed.

It calls for more research into underdeveloped areas, such as model-hardware attacks and malicious model training, and recommends greater use of frameworks and guidance from the NCSC, ETSI, and the UK government’s AI cybersecurity code of practice.

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Brazil’s Ceará state introduces AI assistant for document review

The Junta Comercial do Estado do Ceará has launched an AI-powered document analysis assistant, marking the first public-facing AI service by the Government of the State of Ceará in Brazil. The initiative was announced through an official statement.

The tool is integrated into the Jucec services portal and acts as a pre-analysis system. It reviews documents, cross-checks data and identifies inconsistencies before formal submission.

Officials say the AI system allows users to correct errors in advance, reducing delays and improving efficiency. The analysis is conducted quickly and clearly highlights issues for businesses and accountants.

The initiative is part of wider efforts to modernise public services and support digital transformation in Brazil.

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Powerful Gemini update turns simple prompts into ready-to-use results

Gemini can now generate downloadable and ready-to-share files directly in chat across a wide range of formats, including PDF, Microsoft Word, Excel, Google Docs, Sheets, and Slides.

The new feature is meant to remove the extra steps that often follow AI-assisted brainstorming, such as copying content into other applications and reformatting it manually. Instead, users can ask Gemini to create a structured file that is already formatted and ready to download or export to Google Drive.

Supported formats include Google Workspace files, PDF, DOCX, XLSX, CSV, LaTeX, TXT, RTF, and Markdown. The company says the feature is now available globally to all Gemini app users.

Possible uses include turning budget plans into spreadsheets, organising rough ideas into structured documents, converting long discussions into concise reports, and generating PDF study guides from uploaded lecture notes.

Why does it matter?

What changes here is not simply that Gemini can create more file types, but that it moves AI one step closer to replacing part of the software workflow itself. Instead of using AI to generate rough text and then finishing the task manually in Word, Excel, or Google Docs, users can now get output in a format that is already structured for immediate use.

That may reduce friction between prompting and execution, making AI more useful in everyday work, study, and administration. In practical terms, the update pushes Gemini further from being just a conversational assistant towards becoming a tool that can produce finished digital outputs people can actually work with.

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