As autonomous AI agents move from theory into implementation, their impact on the financial services sector is becoming tangible. A recent whitepaper from IBM Consulting, titled “Agentic AI in Financial Services: Opportunities, Risks, and Responsible Implementation”, outlines how these AI systems—designed for autonomous decision-making and long-term planning—can fundamentally reshape how financial institutions operate. The paper presents a balanced framework that identifies where Agentic AI can add value, the risks it introduces, and how institutions can implement these systems responsibly. Understanding Agentic AI AI agents, in this context, are software entities that interact with their environments to accomplish tasks with a high degree of autonomy. Unlike traditional automation or even LLM-powered chatbots, Agentic AI incorporates planning, memory, and reasoning to execute dynamic tasks across systems. IBM categorizes them into Principal, Service, and Task agents, which collaborate in orchestrated systems. These systems enable the agents to autonomously process information, select tools, and interact with human users or enterprise systems in a closed loop of goal pursuit and reflection. The whitepaper describes the evolution from rule-based automation to multi-agent orchestration, emphasizing how LLMs now serve as the reasoning engine that drives agent behavior in real-time. Crucially, these agents can adapt to evolving conditions and handle complex, cross-domain tasks, making them ideal for the intricacies of financial services. Key Opportunities in Finance IBM identifies three primary use case patterns where Agentic AI can unlock significant value: Customer Engagement & Personalization Agents can streamline onboarding, personalize services through real-time behavioral data, and drive KYC/AML processes using tiered agent hierarchies that reduce manual oversight.Operational Excellence & Governance Agents improve internal efficiencies by automating risk management, compliance verification, and anomaly detection, while maintaining auditability and traceability.Technology & Software Development They support IT teams with automated testing, predictive maintenance, and infrastructure optimization—redefining DevOps through dynamic, self-improving workflows. These systems promise to replace fragmented interfaces and human handoffs with integrated, persona-driven agent experiences grounded in high-quality, governed data products. Risk Landscape and Mitigation Strategies Autonomy in AI brings unique risks. The IBM paper categorizes them under the system’s core components—goal misalignment, tool misuse, and dynamic deception being among the most critical. For instance, a wealth management agent might misinterpret a client’s risk appetite due to goal drift, or bypass controls by chaining permissible actions in unintended ways. Key mitigation strategies include: Goal Guardrails: Explicitly defined objectives, real-time monitoring, and value alignment feedback loops. Access Controls: Least-privilege design for tool/API access, combined with dynamic rate-limiting and auditing. Persona Calibration: Regularly reviewing agents’ behavior to avoid biased or unethical actions. The whitepaper also emphasizes agent persistence and system drift as long-term governance challenges. Persistent memory, while enabling learning, can cause agents to act on outdated assumptions. IBM proposes memory reset protocols and periodic recalibrations to counteract drift and ensure continued alignment with organizational values. Regulatory Readiness and Ethical Design IBM outlines regulatory developments in jurisdictions like the EU and Australia, where agentic systems are increasingly considered “high-risk.” These systems must comply with emerging mandates for transparency, explainability, and continuous human oversight. In the EU’s AI Act, for example, agents influencing access to financial services may fall under stricter obligations due to their autonomous and adaptive behavior. The paper recommends proactive alignment with ethical AI principles even in the absence of regulation—asking not just can we, but should we. This includes auditing agents for deceptive behavior, embedding human-in-the-loop structures, and maintaining transparency through natural language decision narratives and visualized reasoning paths. Conclusion Agentic AI stands at the frontier of enterprise automation. For financial services firms, the promise lies in enhanced personalization, operational agility, and AI-driven governance. Yet these benefits are closely linked to how responsibly these systems are designed and deployed. IBM’s whitepaper serves as a practical guide—advocating for a phased, risk-aware adoption strategy that includes governance frameworks, codified controls, and cross-functional accountability. Check out the White Paper. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit. Mohammad AsjadAsjad is an intern consultant at Marktechpost. He is persuing B.Tech in mechanical engineering at the Indian Institute of Technology, Kharagpur. Asjad is a Machine learning and deep learning enthusiast who is always researching the applications of machine learning in healthcare.Mohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/Critical Security Vulnerabilities in the Model Context Protocol (MCP): How Malicious Tools and Deceptive Contexts Exploit AI AgentsMohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/Stability AI Introduces Adversarial Relativistic-Contrastive (ARC) Post-Training and Stable Audio Open Small: A Distillation-Free Breakthrough for Fast, Diverse, and Efficient Text-to-Audio Generation Across DevicesMohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/Meta AI Introduces CATransformers: A Carbon-Aware Machine Learning Framework to Co-Optimize AI Models and Hardware for Sustainable Edge DeploymentMohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/Enterprise AI Without GPU Burn: Salesforce’s xGen-small Optimizes for Context, Cost, and Privacy 🚨 Build GenAI you can trust. ⭐️ Parlant is your open-source engine for controlled, compliant, and purposeful AI conversations — Star Parlant on GitHub! 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Latest   Machine Learning Beyond the Prompt: What Google’s LLM Advice Doesn’t Quite Tell You 0 like May 18, 2025 Share this post Author(s): Mayank Bohra Originally published on Towards AI. Image by the author Alright, let’s talk about prompt engineering. Every other week, it seems there is a new set of secrets or magical techniques guaranteed to unlock AI perfection. Recently, a whitepaper from Google made the rounds, outlining their take on getting better results from Large Language Models. Look, effective prompting is absolutely necessary. It’s the interface layer, how we communicate our intent to these incredibly powerful, yet often frustrating opaque, models. Think of it like giving instructions to a brilliant but slightly eccentric junior engineer who only understands natural language. You need to be clear, specific, and provide context. But let’s be pragmatic. The idea that a few prompt tweaks will magically “10x” your results for every task is marketing hype, not engineering reality. These models, for all their capabilities, are fundamentally pattern-matching machines operating within a probabilistic space. They don’t understand in the way a human does. Prompting is about nudging that pattern matching closer to the desired outcome. So, what did Google’s advice cover, and what’s the experience builder’s take on it? The techniques generally boil down to principles we’ve known for a while: clarity, structure, providing examples and iteration. The Fundamentals: Clarity, Structure, Context Much of the advice centers on making your intent unambiguous. This is ground zero for dealing with LLMs. They excel at finding patterns in vast amounts of data, but they stumble on vagueness. Being Specific and Detailed: This isn’t a secret; it’s just good communication. If you ask for “information about AI”, you’ll get something generic. If you ask for “a summary of recent advancements in Generative AI model architecture published in research papers since April 2025, focusing on MoE models”, you give the model a much better target. Defining Output Format: Models are flexible text generators. If you don’t specify structure (JSON, bullet points, a specific paragraph format), you’ll get whatever feels statistically probable based on the training data, which is often inconsistent. Telling the model “Respond in JSON format with keys ‘summary’ and ‘key_findings’” isn’t magic; it’s setting clear requirements. Providing Context: Models have limited context windows. Showing your entire codebase or all user documentation in won’t work. You need to curate teh relevant information. This principle is the entire foundation of Retrieval Augmented Generation (RAG), where you retrieve relevant chunks of data and then provide them as context to the prompt. Prompting alone without relevant external knowledge only leverage the model’s internal training data, which might be outdated or insufficient for domain-specific tasks. These points are foundational. They’re less about discovering hidden model behaviors and more about mitigating the inherent ambiguity of natural language and the model’s lack of true world understanding. Structuring the Conversation: Roles and Delimiters Assigning a role (“Act as an expert historian…”) or using delimiters (like “` or — -) are simple yet effective ways to guide the model’s behavior and separate instructions from input. Assigning a Role: This is a trick to prime the model to generate text consistent with a certain persona or knowledge domain it learned during training. It leverage the fact that the model has seen countless examples of different writing styles and knowledge expressions. It works, but it’s a heuristic, not a guarantee of factual accuracy or perfect adherence to the role. Using Delimiters: Essential for programmatic prompting. When you’re building an application that feeds user input into a prompt, you must use delimiters (e.g., triple backticks, XML tags) to clearly separated the user’s potentially malicious input from your system instructions. This is a critical security measure against prompt injection attacks, not just a formatting tip. Nudging the Model’s Reasoning: Few-shot and Step-by-Step Some techniques go beyond just structuring the input; they attempt to influence the model’s internal processing. Few-shot Prompts: Providing a few examples of input/output pairs (‘Input X → Output Y’, Input A → Output B, Input C→ ?’) if often far more effective than just describing the task. Why? Because the model learns the desired mapping from the examples. It’s pattern recognition again. This is powerful for teaching specific formats or interpreting nuanced instructions that hard to describe purely verbally. It’s basically in-context learning. Breaking Down Complex Tasks: Asking the model to think step-by-step (or implementing techniques like Chain-of-Thought or Tree-of-Thought prompting outside the model) encourages it to show intermediate steps. This often leads to more accurate final results, especially for reasoning-heavy tasks. Why? It mimics hwo humans solve problems and forces the model to allocate computational steps sequentially. It’s less about a secret instruction and more about guiding the model through a multi-step process rather than expecting it to leap to the answer in one go. The Engineering Angle: Testing and Iteration The advice also includes testing and iteration. Again, this isn’t unique to prompt engineering. It’s fundamental to all software development. Test and Iterate: You write a prompt, you test it with various inputs, you see where it fails or is suboptimal, you tweak the prompt, and you test again. This loop is the reality of building anything reliable with LLMs. It highlights that prompting is often empirical; you figure out what works by trying it. This is the opposite of a predictable, documented API. The Hard Truth: Where Prompt Engineering Hits a Wall Here’s where the pragmatic view really kicks in. Prompt engineering, while crucial, has significant limitations, especially for building robust, production-grade applications: Context Window Limits: There’s only so much information you can cram into a prompt. Long documents, complex histories, or large datasets are out. This is why RAG systems are essential — they manage and retrieve relevant context dynamically. Prompting alone doesn’t solve the knowledge bottleneck. Factual Accuracy and Hallucinations: No amount of prompting can guarantee a model won’t invent facts or confidently present misinformation. Prompting can sometimes mitigate this by, for examples, telling the model to stick only to the provided context (RAG), but it doesn’t fix the underlying issue that the model is a text predictor, not a truth engine. Model Bias and Undesired Behavior: Prompts can influence output, but they can’t easily override biases embedded in the training data or prevent the model from generating harmful or inappropriate content in unexpected ways. Guardrails need to be implemented *outside* the prompt layer. Complexity Ceiling: For truly complex, multi-step processes requiring external tool use, decision making, and dynamic state, pure prompting breaks down. This is the domain of AI agents, which use LLMs as the controller but rely on external memory, planning modules, and tool interaction to achieve goals. Prompting is just one part of the agent’s loop. Maintainability: Try managing dozens or hundreds of complex, multi-line prompts across different features in a large application. Versioning them? Testing changes? This quickly becomes an engineering nightmare. Prompts are code, but often undocumented, untestable code living in strings. Prompt Injection: As mentioned with delimiters, allowing external input (from users, databases, APIs) into a prompt opens the door to prompt injection attacks, where malicious input hijacks the model’s instructions. Robust applications need sanitization and architectural safeguards beyond just a delimiter trick. What no one tells you in the prompt “secrets” articles is that the difficulty scales non-linearly with the reliability and complexity required. Getting a cool demo output with a clever prompt is one thing. Building a feature that consistently works for thousands of users on diverse inputs while being secure and maintainable? That’s a whole different ballgame. The Real “Secret”? It’s Just Good Engineering. If there’s any “secret” to building effective applications with LLMs, it’s not a prompt string. It’s integrating the model into a well-architected system. This involves: Data Pipelines: Getting the right data to the model (for RAG, fine-tuning, etc.). Orchestration Frameworks: Using tools like LangChain, LlamaIndex, or building custom workflows to sequence model calls, tool use, and data retrieval. Evaluation: Developing robust methods to quantitatively measure the quality of LLM output beyond just eyeballing it. This is hard. Guardrails: Implementing safety checks, moderation, and input validation *outside* the LLM call itself. Fallback Mechanisms: What happens when the model gives a bad answer or fails? Your application needs graceful degradation. Version Control and Testing: Treating prompts and the surrounding logic with the same rigor as any other production code. Prompt engineering is a critical *skill*, part of the overall toolkit. It’s like knowing how to write effective SQL queries. Essential for database interaction, but it doesn’t mean you can build a scalable web application with just SQL. You need application code, infrastructure, frontend, etc. Wrapping Up So, Google’s whitepaper and similar resources offer valuable best practices for interacting with LLMs. They formalize common-sense approaches to communication and leverage observed model behaviors like few-shot learning and step-by-step processing. If you’re just starting out, or using LLMs for simple tasks, mastering these techniques will absolutely improve your results. But if you’re a developer, an AI practitioner, or a technical founder looking to build robust, reliable applications powered by LLMs, understand this: prompt engineering is table stakes. It’s necessary, but far from sufficient. The real challenge, the actual “secrets” if you want to call them that, lie in the surrounding engineering — the data management, the orchestration, the evaluation, the guardrails, and the sheer hard work of building a system that accounts for the LLM’s inherent unpredictability and limitations. Don’t get fixated on finding the perfect prompt string. Focus on building a resilient system around it. That’s where the real progress happens. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI Towards AI - Medium Share this post

Forward-thinking businesses – and even nations – are upskilling mid-career professionals to help them not only survive but prosper in the era of widespread enterprise artificial intelligence (AI). As businesses and public sector organisations adopt AI at a lightning pace, white-collar professions face huge disruption, not unlike that experienced by nineteenth century blue-collar workers. According to research by OpenAI and the University of Pennsylvania, roles that will be affected include accountants, legal assistants, financial analysts, journalists, translators and public relations professionals. Meanwhile, Goldman Sachs published figures in March 2023 that spoke of 300 million jobs exposed to AI across all sectors. Although it’s a concept dating back decades, the widespread take-up of generative AI (GenAI) began around 2022 with the release of ChatGPT. It was a wake-up call for governments and businesses alike, which must prepare for inevitable disruption. According to Tram Anh Nguyen, co-founder of the centre for finance, technology and entrepreneurship (CFTE), people over the age of 40 in mid-career professional roles are the most at risk of major job disruption as businesses integrate AI into their operations. CFTE is a global education platform that specialises in training in the finance sector, including teaching AI in finance. Nguyen, who is also Global Women in AI chair, spent decades working in the finance sector in business roles, said: “AI is no longer a future concept. It’s here and it’s affecting everyone at every level.” But this does not mean professionals will be replaced if they are re-trained – and this does not just mean technical training. Training on AI for non-technical roles will encompass professionals learning underlying knowledge about AI, the AI tools available to them and the use cases for AI in their roles, said Nguyen. In its whitepaper titled The AI-fication of talents, CFTE said three groups of professionals will emerge. It reported that there will be: “mass displacement” of roles centred on execution which will be increasingly automated; “supercharged professionals” will emerge who use AI to expand scope and scale; while “creative disruptors” will be small group inventing new models, products and systems. Nguyen warned that the UK is behind in readying the workforce for AI. “We are not preparing people in the right way because the focus is not on adult education at scale,” she told Computer Weekly. She cited a project in Singapore which CFTE helped to design. It involves supporting mid-career transitions, particularly focusing on technology and finance sectors, while providing financial support to help people switching careers. All Singaporeans aged 40 and above received funds to refresh their skills, with a large proportion taking up IT-related courses in areas including artificial intelligence. Around 555,000 people participated in programmes supported by SkillsFuture Singapore (SSG) in 2024 and 520,000 in 2023, according to Singapore newspaper The Straits Times. These are significant numbers for Singapore, which has a population of around six million, but the same challenge is faced globally. One sector being heavily affected is financial services, which leads the way in AI innovation and investment. For example, Bloomberg Intelligence recently put the number of jobs set to be replaced by AI in the US finance sector – Wall Street specifically – at hundreds of thousands. CIOs questioned by the organisation expected 3% of their workforce to be cut on average. Around a quarter of respondents expect the workforce to be cut by between 5% and 10% as AI takes over roles, with the back and middle offices to be most affected. According to research by banking industry benchmarking firm Evident, AI-related roles could be the only “safe jobs” in the banking sector as financial organisations “relentlessly” press on with AI-led transformation. It’s banking industry report found that recruitment of AI development professionals grew by 6% in the last year, hiring of data engineers increased by 14%, and the number of AI and software implementation experts hired increased by 42%. But while the finance sector finds itself on the front-line of the AI revolution, the technology’s rapid spread goes way beyond. Bloomberg’s head of AI, Amanda Stent, recently told Computer Weekly in an interview that there has been “no revolution in history that has not led to job transformation”. “Some types of job change, some types of job go away,” they added. “But there’s also no revolution in history that hasn’t led to more jobs created overall, I think that is true with AI, which will augment a lot of people.” Stent said all workers, regardless of their roles, will have to learn to use AI: “We can teach people how to be effective users of AI without needing to know all that maths.” The legal sector is an example of a traditional industry adapting to AI. The UK regulator of solicitors, the Solicitors Regulation Authority, recently authorised the first law firm to provide legal services purely through AI. Mark Lewis, a lawyer at Stephenson Hardwood, specialised in technology, said most, “if not virtually all, serious law firms” are deploying AI and GenAI operationally. “Typical use cases include document review, analysis and summarisation, legal research, case research and predicting the outcomes of cases, reviewing and reporting on the application of regulations around the world, and, of course, in law firm back-office operations – for example, in client due diligence and acceptance.” But he added that AI is not “yet” causing “major disruption” in the legal sector: “As with talk of AI disrupting many sectors, including doing away with the work now done by paralegals, junior lawyers, and even senior lawyers, this hasn’t really happened yet in the legal markets here – or, I think, anywhere. “There is a good deal of the usual tech hype about it. No doubt AI will become integral to legal process and lawyering at all levels, but, as in many other sectors, even that is going to take time and the maturing of legal use cases.” He said firms are, however, preparing for the impact of AI: “We, like many firms, have made available to all our lawyers GenAI tools developed specifically for us, to be used within certain parameters and in accordance with our AI/GenAI policies. “We want our lawyers to use these GenAI tools, to become accustomed to the way it processes work, to understand its strengths and limitations, and to become expert in creating and refining prompts. “For me, there is an even more important – existential – point: the single biggest challenge is how we as a society learn to understand, live and work with AI. It should start as early as possible and continue through our lives.” In the IT sector, AI is a huge business opportunity, but the technology is also transforming how suppliers operate. Workers in the IT sector will also have to learn to work with AI. Amrinder Singh, head of EMEA and APAC operations at Indian IT services firm Hexaware, told Computer Weekly that all the company’s staff, around 30,000, will be trained how to harness AI. He put it in startling terms the risks to workers that are not trained up. “We said that there is no future for single-skilled people,” he said. “Unless you are multi-skilled with domain understanding, as well as understanding how to use AI and technology, you will not survive.” Read more about GenAI

The UK government has outlined how it will utilise the new electronic visa (eVisa) system and “modern biometric technology” to support immigration enforcement and “strengthen the border”. Published 12 May 2025, the Home Office’s 82-page immigration whitepaper – titled Restoring control over the immigration system – contains a range of proposals for how the UK government will use data-driven technologies to track migrants and clamp down on “visa abuse” by those staying and working in the country illegally. During a press conference the same day, prime minister Keir Starmer said the whitepaper “is absolutely central to my Plan for Change”, and that it will allow the government to “take back control of our borders” after net migration quadrupled between 2019 and 2023. “Nations depend on rules – fair rules. Sometimes they’re written down, often they’re not, but either way, they give shape to our values,” he said. “Now, in a diverse nation like ours, and I celebrate that, these rules become even more important. Without them, we risk becoming an island of strangers, not a nation that walks forward together.” The whitepaper outlined how a key plank of the government’s overall approach would be using “newly gathered intelligence” from the UK’s new eVisa system – which has so far been plagued by data quality and integrity problems – to keep track of who is allowed to be in the country. “The move to digital evidence of immigration status will enable us to update records in real time when status changes, ensuring those who are no longer entitled to access public services, work or rent will have this reflected on their eVisa, rather than continuing to hold physical evidence of status which is no longer up to date,” it said, adding the intelligence provided by digital visas will allow the state to “maintain and increase contact” with people as they move through the immigration system. “Put together, the comprehensive, intelligence-led and effective roll-out of eVisas to all foreign nationals resident in the UK will have a transformative impact on our immigration controls: telling us when each individual leaves the country and when they have returned; telling us whether they have the right to work, to rent, to claim benefits or use public services, and telling us how long they have the right to stay. “Importantly, eVisas will make it much easier for Immigration Enforcement to identify those who try to stay and work in the UK illegally, to track them down and take action against them.” The government added it will also “continue to harness the latest developments in artificial intelligence [AI], facial recognition and age assessment technologies” to gather “the most accurate information” possible on every individual entering the UK, and that work is ongoing to enhance the accuracy and quality of the data held to ensure people’s status information remains up to date. Enny Choudhury, co-head of legal at the Joint Council for the Welfare of Immigrants (JCWI), said the government’s push to expand eVisa and biometric surveillance “is yet another step towards a dystopian immigration regime where people who’ve made the UK home are tracked, monitored and targeted simply because of their immigration status”. She added that the tools have nothing to do with security and are instead about giving the illusion of control: “Used alongside immigration raids and enforcement crackdowns, they will deepen mistrust, isolate communities, and expose people to errors and abuse in an already chaotic system. The eVisa roll-out has already shown itself to be riven with errors, and has left many unable to prove their status. “If ministers were serious about fairness, they’d invest in clear, compassionate immigration routes – not surveillance infrastructure that treats people as threats, not neighbours.”Computer Weekly contacted the Home Office about the criticisms levied against the whitepaper’s technology proposals, but received no response. While the government claims in the whitepaper that “the transition to eVisa has been successfully providing a significantly better end-to-end experience for individuals throughout their entire journey”, the system ran into problems almost immediately. Within the first few weeks of the eVisa system going live, for example, many reported issues when flying back to the UK, with travellers struggling to prove their immigration status to airport staff. Others have reported issues from within the UK as well, including with GPs not accepting the share codes issued via their UK Visas and Immigration (UKVI) digital account, which people are supposed to be able to use to prove their immigration status when dealing with a range of third parties, including employers and letting agencies. The issues are also affecting refugees, who are reportedly having problems connecting their passports to their online visa, according to digital rights groups supporting them. Other refugees are also unable to set up or log in to their UKVI accounts – which they need to set up a bank account, claim benefits or rent housing – as they have not been forwarded the necessary details by the Home Office. “As a result of the flawed e-visa scheme, people with the legal right to be in the UK have been held at airports, denied jobs and even made homeless. Others are having to rely on [Biometric Resident Permit] documents that expired over five months ago,” said Sara Alsherif, the migrant rights programme manager at Open Rights Group (ORG). “It is outrageous that the government has the audacity to refer to the shambolic eVisa scheme as ‘successful’. But it’s beyond comprehension that they are considering relying on this flawed scheme to carry out raids and deport people.  “With the use of technology, automated decision-making and AI, we can expect to see a Windrush scandal on steroids, and the Labour government really needs to ask whether it wants to be the architect of such human rights abuses.” Digital rights campaigners have long contended that the online-only, real-time nature of the Home Office’s eVisa scheme – which trawls dozens of disparate government databases to generate a new immigration status each time someone logs in – is error-prone and “deeply problematic”. “When users enter their details to log into the Government View and Prove system [in their UKVI account], they are not accessing their status directly, but rather their credentials are being used to search and retrieve dozens of different records held on them across different databases,” said ORG in a September 2024 report. It added that research has identified more than 90 different platforms and casework systems that immigration data may be pulled from within the UKVI ecosystem to determine a person’s status: “View and Prove uses an algorithmic and probabilistic logic to determine which data to extract and which e-records to use when it encounters multiple records, i.e. in instances where people have renewed or changed their immigration status, or appealed an incorrect decision. “It is these real-time and opaque automated checks that generate a person’s immigration status, which they can then share with an employer, landlord or international carrier.”  The ORG said the online-only design choice creates multiple problems for users, including making it “impossible” for an individual to be certain that they will get a correct result on any particular occasion; increased potential for incorrect decisions as a result of people’s records being pulled from “numerous servers”: and the details of two different people being conflated in instances where they, for example, share the same name or date of birth. In its whitepaper, the government also outlined proposals to deploy “modern biometric technology” to frontline immigration enforcement officers, specifically highlighting that they will play a role in facilitating immigration raids. It added that, over the coming months, it would also roll out bodyworn video cameras to frontline teams, “together with an advanced data management system and improved mobile biometric kits, improving identity verification, transparency, accountability and officer safety”. It claimed that, taken together, “these improvements will provide an objective record of interactions, strengthen evidence gathering and increase public confidence in enforcement activity while supporting the professional standards of our staff”. According to a blog post published by home secretary Yvette Cooper – which does not mention the extensive tech-related proposals contained in the whitepaper – the new requirements laid out in the document will “[restore] order to a failed system that saw net migration quadruple between 2019 and 2023.” These measures include raising the skilled worker threshold, ending overseas recruitment for social care visas, reducing the length of time graduates can stay in the UK after studying, new penalties for businesses employing workers illegally, and streamlining the deportation process to further increase “returns of foreign national offenders”. The government has also outlined how it will prevent the “dependents” of immigrants from coming to the country if they are not proficient enough in English. Fizza Qureshi, CEO of the Migrants’ Rights Network told Computer Weekly that “immigration raids are a racist fear mechanism that disproportionately impact migrant and racialised communities”, and that the use of eVisas, Electronic Travel Authorizations (ETAs) and increased biometric data collection has been “an insidious tool” to create a database of migrants. “We were unsure of how it would be used to further surveil migrants and intensify enforcement operations,” she said. “Now, we finally know the measures set out in the new immigration whitepaper will be weaponised to further target and terrorise migrants and racialised people.” Responding to the whitepaper, trade unions and trade associations highlighted how the proposed measures could also undermine the UK’s ambitions to create a thriving, world-leading technology sector by undercutting access to talent and skills. “Continually increasing visa costs and requirements has the potential to undermine efforts to attract critically important collaboration and could undermine success in AI, tech, science, engineering and a host of other areas,” said Sue Ferns, deputy general secretary at the Prospect union. Antony Walker, deputy CEO of TechUK, added that the UK tech sector’s continued success is linked to the diverse talent it attracts from around the world: “As the demand for skilled workers in fields such as AI, cyber security, and quantum continues to grow, it is crucial that the UK grants and maintains immigration pathways that enable tech companies to access the talent they need. “A well-designed and fairly priced visa system is essential to maintaining the UK’s global competitiveness. We have the opportunity to reassess the UK’s immigration system to enhance public confidence and better support businesses. In particular, reviewing costs associated with visas and other related charges such as the Immigration Skills Charge could help ensure the system is not only fair but also effective. “If government wants to reduce reliance on the immigration system, it must urgently invest in skills and training, otherwise businesses will be left without the workforce they need to survive and grow.” Read more about immigration and technology Interview: Petra Molnar, author of ‘The walls have eyes’: Refugee lawyer and author Petra Molnar speaks to Computer Weekly about the extreme violence people on the move face at borders across the world, and how increasingly hostile anti-immigrant politics is being enabled and reinforced by a ‘lucrative panopticon’ of surveillance technologies. Greek authorities subject refugees to invasive surveillance: Greek border authorities are subjecting asylum seekers to invasive phone confiscations and artificial intelligence-powered surveillance, in another potential violation of European data protection laws. English Channel surveillance used ‘to deter and punish migrants’: Instead of opening safe and legal routes to the UK, the country’s border control ecosystem is deploying surveillance technologies in the English Channel to deter migrant crossings, it is claimed.