AI數(shù)據(jù)泄露與影子AI：2025年英國組織面臨的法律雷區(qū)

Camilo Artiga-Purcell, General Counsel at Kiteworks, identifies some of the ever-increasing risks and potential consequences of rushing to use AI in legal practice
Kiteworks的總法律顧問卡米洛·阿蒂加-珀塞爾指出了在法律實踐中急于使用人工智能所帶來的一些日益增加的風(fēng)險和潛在后果。
 
Picture a partner at a leading UK law firm, racing to finalise a high-stakes merger. With a deadline looming, they turn to a free online AI tool, uploading sensitive deal documents for rapid analysis. The tool delivers, and the work is completed on time. Months later, a rival firm using the same AI platform receives uncannily precise insights about the merger’s structure in an AI-generated response. An investigation reveals that the original documents were incorporated into the AI’s training data, inadvertently exposing confidential strategies. The fallout is swift: a regulatory probe, eroded client trust, and a legal battle over compromised attorney-client privilege.
想象一下，英國一家頂尖律師事務(wù)所的合伙人正在爭分奪秒地敲定一項高風(fēng)險的并購交易。由于截止日期臨近，他們轉(zhuǎn)而使用一款免費的在線人工智能工具，上傳敏感的交易文件以進行快速分析。該工具完成了任務(wù)，工作得以按時完成。幾個月后，另一家使用同一人工智能平臺的競爭律所，在人工智能生成的回復(fù)中收到了關(guān)于該并購結(jié)構(gòu)異常精準(zhǔn)的見解。調(diào)查顯示，原始文件已被納入人工智能的訓(xùn)練數(shù)據(jù)，無意中泄露了機密策略。后果接踵而至：監(jiān)管調(diào)查、客戶信任受損，以及一場圍繞受損的律師-客戶特權(quán)的法律糾紛。
 
This scenario is not a hypothetical – it reflects a growing crisis across UK organisations. Legal departments and businesses are embracing artificial intelligence at an unprecedented rate, driven by its promise of efficiency in tasks like contract drafting and legal research. Yet, a survey of 300 corporate legal departments found that 81% are using unapproved AI tools without data controls, creating a legal and compliance minefield. For UK organisations, governed by the UK GDPR and facing emerging AI regulations, the risks are acute. Without action, legal teams face breaches of confidentiality, multimillion-pound fines, and reputational damage. This article explores the scale of this problem, its legal implications, and practical steps to safeguard sensitive data while leveraging AI responsibly.
這種情況并非假設(shè)——它反映了英國各組織正面臨的一個日益嚴(yán)重的危機。在人工智能有望提高合同起草和法律研究等任務(wù)效率的推動下，法律部門和企業(yè)正以前所未有的速度采用人工智能。然而，一項針對300個企業(yè)法律部門的調(diào)查發(fā)現(xiàn)，81%的部門在使用未經(jīng)批準(zhǔn)的人工智能工具，且沒有數(shù)據(jù)管控措施，這造成了一個法律和合規(guī)方面的雷區(qū)。對于受英國《通用數(shù)據(jù)保護條例》約束且面臨新興人工智能法規(guī)的英國組織來說，相關(guān)風(fēng)險十分嚴(yán)峻。若不采取行動，法律團隊可能會面臨機密泄露、數(shù)百萬英鎊的罰款以及聲譽受損等問題。本文探討了這一問題的嚴(yán)重程度、其法律影響，以及在負(fù)責(zé)任地利用人工智能的同時保護敏感數(shù)據(jù)的切實步驟。
 
Scale of the Problem 問題的嚴(yán)重性
 
The adoption of AI in UK legal departments is surging, with tools promising to streamline contract reviews, legal research, and document analysis. However, this enthusiasm has birthed a dangerous trend known as “Shadow AI,” where employees use personal or unapproved AI tools for work tasks without oversight. According to a recent survey, 83% of in-house counsel use AI tools not provided by their organisations, and 47% operate without any governance policies. The Stanford AI Index Report highlights a 56% rise in AI-related incidents globally, with data leaks a primary concern. In the UK, 57% of organisations admit they cannot track sensitive data exchanges involving AI, amplifying the risk of breaches.
英國法律部門對人工智能的采用率正大幅上升，相關(guān)工具有望簡化合同審查、法律研究和文檔分析工作。然而，這種熱情催生了一種被稱為“影子人工智能”的危險趨勢，即員工在未經(jīng)監(jiān)督的情況下使用個人或未經(jīng)批準(zhǔn)的人工智能工具處理工作任務(wù)。最近的一項調(diào)查顯示，83%的內(nèi)部法律顧問使用并非由其所在組織提供的人工智能工具，47%的人在沒有任何治理政策的情況下使用這些工具?！端固垢Ｈ斯ぶ悄苤笖?shù)報告》強調(diào)，全球與人工智能相關(guān)的事件增加了56%，數(shù)據(jù)泄露是主要關(guān)注點。在英國，57%的組織承認(rèn)他們無法追蹤涉及人工智能的敏感數(shù)據(jù)交換，這加劇了數(shù)據(jù)泄露的風(fēng)險。
 
We recently surveyed 461 organisations on this issue, across a range of industries, and the results reinforce these concerns with alarming specificity. Only 17% have automated controls with data loss prevention capabilities to block unauthorised AI access though the legal sector fares even worse, with just 15% implementing technical controls – the lowest of any industry surveyed. Perhaps most troubling for UK law firms, 38% of legal organisations admit that over 16% of data sent to AI tools contains private or sensitive information, with 23% reporting that more than 30% of their AI-processed data is private.
我們最近就這一問題對461家不同行業(yè)的機構(gòu)進行了調(diào)查，結(jié)果以驚人的具體性印證了這些擔(dān)憂。只有17%的機構(gòu)擁有具備數(shù)據(jù)防泄漏功能的自動化控制措施，以阻止對人工智能的未授權(quán)訪問，而法律行業(yè)的情況更糟，僅有15%的機構(gòu)實施了技術(shù)控制措施——這在所有接受調(diào)查的行業(yè)中是最低的?；蛟S對英國律師事務(wù)所而言最令人擔(dān)憂的是，38%的法律機構(gòu)承認(rèn)，發(fā)送給人工智能工具的數(shù)據(jù)中，超過16%包含私人或敏感信息，23%的機構(gòu)表示，其經(jīng)人工智能處理的數(shù)據(jù)中，超過30%是私人信息。
 
The UK’s regulatory landscape heightens these challenges. The UK GDPR, aligned with the EU’s GDPR, imposes stringent obligations on data processing, storage, and cross-border transfers, with fines up to £17.5 million or 4% of global annual turnover for violations. The proposed UK AI Bill signals increased scrutiny of AI governance, while existing regulations like the Network and Information Systems (NIS2) Directive demand robust cybersecurity. For legal departments, a single employee uploading client data to an unapproved AI tool can expose privileged communications, trade secrets, or merger strategies to servers in unknown jurisdictions, undermining the foundations of legal practice.
英國的監(jiān)管環(huán)境加劇了這些挑戰(zhàn)。與歐盟《通用數(shù)據(jù)保護條例》（GDPR）保持一致的英國《通用數(shù)據(jù)保護條例》（UK GDPR），對數(shù)據(jù)處理、存儲和跨境傳輸施加了嚴(yán)格的義務(wù)，違規(guī)者將面臨最高1750萬英鎊或全球年營業(yè)額4%的罰款。擬議的英國《人工智能法案》預(yù)示著對人工智能治理的審查將加強，而《網(wǎng)絡(luò)與信息系統(tǒng)（NIS2）指令》等現(xiàn)有法規(guī)則要求健全的網(wǎng)絡(luò)安全措施。對于法律部門而言，哪怕有一名員工將客戶數(shù)據(jù)上傳至未經(jīng)批準(zhǔn)的人工智能工具，都可能導(dǎo)致特權(quán)通信、商業(yè)秘密或并購策略暴露在未知司法管轄區(qū)的服務(wù)器上，從而動搖法律執(zhí)業(yè)的根基。
 
Legal and Compliance Risks 法律與合規(guī)風(fēng)險
 
The legal and compliance risks of ungoverned AI use are profound for UK organisations. Data protection violations top the list. The UK GDPR requires organisations to establish a lawful basis for processing personal data, adhere to data minimisation principles, and ensure security by design. When lawyers upload client data to consumer AI tools like ChatGPT or Claude, they relinquish control over that information. The data may be processed via third-party APIs, stored on servers in multiple jurisdictions, or used to train AI models, all potentially breaching UK GDPR requirements. Such violations can trigger severe penalties and lasting reputational harm.
對于英國組織而言，不受管控地使用人工智能所帶來的法律和合規(guī)風(fēng)險是深遠(yuǎn)的。數(shù)據(jù)保護違規(guī)位居榜首。《英國通用數(shù)據(jù)保護條例》要求組織為處理個人數(shù)據(jù)確立合法依據(jù)，遵守數(shù)據(jù)最小化原則，并確保設(shè)計層面的安全性。當(dāng)律師將客戶數(shù)據(jù)上傳至ChatGPT或Claude等消費級人工智能工具時，他們就失去了對這些信息的控制權(quán)。這些數(shù)據(jù)可能通過第三方應(yīng)用程序接口進行處理，存儲在多個司法管轄區(qū)的服務(wù)器上，或被用于訓(xùn)練人工智能模型，所有這些都可能違反《英國通用數(shù)據(jù)保護條例》的要求。此類違規(guī)行為可能引發(fā)嚴(yán)厲的處罰和持久的聲譽損害。
 
Confidentiality and privilege concerns are equally grave. Attorney-client privilege, a bedrock of legal practice, can be waived when communications are shared with third-party AI providers. Consider when a UK litigation team uploaded privileged strategies to an AI tool, only to have opposing counsel argue successfully that privilege was lost, rendering years of communications discoverable. Trade secrets and intellectual property face similar risks, as AI platforms may inadvertently expose proprietary information through model outputs or data breaches, violating confidentiality agreements.
保密性和特權(quán)方面的擔(dān)憂同樣嚴(yán)重。律師-客戶特權(quán)作為法律實務(wù)的基石，在與第三方人工智能提供商共享通信內(nèi)容時可能會被放棄。試想一下，英國一個訴訟團隊將享有特權(quán)的策略上傳到某個人工智能工具后，對方律師成功辯稱該特權(quán)已喪失，導(dǎo)致多年的通信內(nèi)容都可能被披露。商業(yè)秘密和知識產(chǎn)權(quán)也面臨類似風(fēng)險，因為人工智能平臺可能會通過模型輸出或數(shù)據(jù)泄露無意中泄露專有信息，從而違反保密協(xié)議。
 
Regulatory compliance failures add further complexity. The NIS2 Directive mandates robust cybersecurity controls, while the Financial Conduct Authority (FCA) requires strict data governance for financial services firms. The Solicitors Regulation Authority (SRA) imposes ethical obligations under Rule 2.1, requiring solicitors to maintain competence in the technologies they use. Failure to understand AI risks can lead to disciplinary action, as seen in recent SRA investigations into tech mismanagement, where firms faced fines and reputational damage for inadequate data security. As AI regulations evolve, legal departments that fail to govern AI use risk becoming targets for enforcement actions. In this case, “Attorney-client privilege can be lost with a single upload.”
監(jiān)管合規(guī)失敗進一步增加了復(fù)雜性?！毒W(wǎng)絡(luò)與信息系統(tǒng)安全指令2》（NIS2）要求實施強有力的網(wǎng)絡(luò)安全控制，而英國金融行為監(jiān)管局（FCA）則要求金融服務(wù)公司實施嚴(yán)格的數(shù)據(jù)治理。英國律師監(jiān)管局（SRA）根據(jù)第2.1條規(guī)則規(guī)定了道德義務(wù)，要求律師對其使用的技術(shù)保持專業(yè)能力。對人工智能風(fēng)險的不了解可能導(dǎo)致紀(jì)律處分，最近SRA對技術(shù)管理不善的調(diào)查就體現(xiàn)了這一點，在這些調(diào)查中，公司因數(shù)據(jù)安全不足而面臨罰款和聲譽損失。隨著人工智能法規(guī)的不斷發(fā)展，未能對人工智能使用進行治理的法律部門可能會成為執(zhí)法行動的目標(biāo)。在這種情況下，“一次上傳就可能失去律師-客戶特權(quán)。”
 
How AI Data Leaks Occur 人工智能數(shù)據(jù)泄露如何發(fā)生
 
AI data leaks stem from a mix of technical vulnerabilities and human error. When lawyers upload documents to consumer AI tools, the data may be used to train the AI model, stored indefinitely on external servers, or shared with third-party APIs without transparency. These platforms, not designed for the rigorous security needs of legal work, make it nearly impossible to retrieve or delete data once uploaded – a risk coined the “irrevocability problem.” This is particularly alarming for legal departments handling privileged or sensitive information.
人工智能數(shù)據(jù)泄露源于技術(shù)漏洞和人為失誤的共同作用。當(dāng)律師將文件上傳到消費級人工智能工具時，這些數(shù)據(jù)可能會被用于訓(xùn)練人工智能模型、在外部服務(wù)器上無限期存儲，或者在缺乏透明度的情況下與第三方應(yīng)用程序接口共享。這些平臺并非為滿足法律工作嚴(yán)苛的安全需求而設(shè)計，一旦數(shù)據(jù)上傳，幾乎無法檢索或刪除——這種風(fēng)險被稱為“不可撤銷問題”。對于處理特權(quán)信息或敏感信息的法律部門而言，這一點尤其令人擔(dān)憂。
 
Common scenarios include lawyers using AI for contract drafting, legal research, or document analysis under tight deadlines. A junior associate might paste a draft settlement agreement into an unapproved AI tool to refine its language, unaware that the data is now stored on a server abroad. Similarly, a senior lawyer might use AI to summarise merger documents, not realising that the tool’s outputs could later reveal confidential strategies to client competitors, targets, or potential buyers. These actions, driven by the need for efficiency, create vulnerabilities that can lead to data leaks, regulatory violations, loss of privilege, or loss of bonafide competitive advantage.
常見場景包括律師在緊迫的截止日期下使用人工智能進行合同起草、法律研究或文檔分析。一位初級律師可能會將一份和解協(xié)議草案粘貼到未經(jīng)批準(zhǔn)的人工智能工具中以優(yōu)化其措辭，卻不知道這些數(shù)據(jù)現(xiàn)在存儲在國外的服務(wù)器上。同樣，一位高級律師可能會使用人工智能來總結(jié)并購文件，卻沒有意識到該工具的輸出后來可能會向客戶的競爭對手、目標(biāo)公司或潛在買家泄露機密策略。這些出于提高效率需求的行為會產(chǎn)生漏洞，可能導(dǎo)致數(shù)據(jù)泄露、違反法規(guī)、特權(quán)喪失或真正競爭優(yōu)勢的喪失。
 
Our recent survey cited above confirmed that these scenarios reflect current industry realities. Despite the legal profession’s heightened awareness of data risks – 31% of legal firms cite data leaks as their top AI concern, the highest of any sector – this awareness hasn’t translated into action: 15% of legal organisations operate with no formal AI data policies whatsoever, while 70% rely solely on human-dependent controls like training sessions and warning emails. This creates what the report calls an “awareness-action gap,” where firms recognise the danger but fail to implement the technical safeguards necessary to prevent catastrophic breaches.
我們上述提到的近期調(diào)查證實，這些場景反映了當(dāng)前行業(yè)的實際情況。盡管法律行業(yè)對數(shù)據(jù)風(fēng)險的意識有所提高——31%的律師事務(wù)所將數(shù)據(jù)泄露列為他們對人工智能的首要擔(dān)憂，這一比例在所有行業(yè)中最高——但這種意識并未轉(zhuǎn)化為行動：15%的法律機構(gòu)完全沒有正式的人工智能數(shù)據(jù)政策，70%的機構(gòu)僅依賴于培訓(xùn)課程和警示郵件等依賴人工的管控措施。這就造成了報告中所說的“意識-行動差距”，即律所認(rèn)識到了危險，卻未能實施必要的技術(shù)防護措施來防止災(zāi)難性的數(shù)據(jù)泄露。
 
Real-World Scenarios 現(xiàn)實場景
 
The dangers of AI data leaks become clear when we imagine what could go wrong. Picture the scenario from our opening: a legal team uploads confidential merger documents to an AI tool for analysis. The platform uses those documents to train its model, and suddenly, sensitive deal information surfaces elsewhere − triggering expensive disputes and destroying client relationships.
當(dāng)我們想象可能出現(xiàn)的問題時，人工智能數(shù)據(jù)泄露的危險就變得清晰起來。想象一下我們開篇提到的場景：一個法律團隊將機密的合并文件上傳到人工智能工具進行分析。該平臺使用這些文件來訓(xùn)練其模型，突然間，敏感的交易信息出現(xiàn)在其他地方——引發(fā)昂貴的糾紛并破壞客戶關(guān)系。
 
Consider another possibility: a UK company’s legal department runs personal data through an unauthorised AI tool. The result? A full GDPR investigation, hefty fines, and damaging headlines that tarnish the firm’s reputation. Perhaps most alarming is this scenario: a litigation team uploads privileged attorney-client communications to an AI platform. When opposing counsel discovers this, they successfully argue that privilege has been waived. The entire case strategy unravels, and what should have been protected conversations become fair game in court.
再考慮另一種可能性：一家英國公司的法律部門通過未經(jīng)授權(quán)的人工智能工具處理個人數(shù)據(jù)。結(jié)果會怎樣？全面的《通用數(shù)據(jù)保護條例》調(diào)查、高額罰款，以及有損公司聲譽的負(fù)面新聞標(biāo)題?；蛟S最令人擔(dān)憂的是這種情況：一個訴訟團隊將享有特權(quán)的律師與客戶的通信上傳到人工智能平臺。當(dāng)對方律師發(fā)現(xiàn)這一點時，他們會成功辯稱特權(quán)已被放棄。整個案件策略會瓦解，本應(yīng)受到保護的對話在法庭上會變成公開可利用的信息。
 
These aren’t just theoretical risks, they represent the very real consequences that await organisations operating without proper AI governance. Each scenario shows how quickly simple upload can transform into a professional catastrophe.
這些并非只是理論上的風(fēng)險，它們代表著那些缺乏適當(dāng)人工智能治理的組織將面臨的非常真實的后果。每個場景都展示了一次簡單的上傳會多么迅速地演變成一場重大的職業(yè)災(zāi)難。
 
Building a Compliant AI Framework 構(gòu)建合規(guī)的人工智能框架
 
To mitigate these risks, UK legal departments must establish a robust AI governance framework tailored to their needs. The foundation is a clear governance structure. Comprehensive AI usage policies should outline acceptable tools, data handling protocols, and consequences for non-compliance, addressing confidentiality, privilege, and data security. Regular risk assessments are vital to identify vulnerabilities, while a formal approval process ensures only secure, compliant AI platforms are used.
為了緩解這些風(fēng)險，英國法律部門必須建立一個符合自身需求的強大人工智能治理框架。其基礎(chǔ)是清晰的治理結(jié)構(gòu)。全面的人工智能使用政策應(yīng)明確可接受的工具、數(shù)據(jù)處理協(xié)議以及違規(guī)后果，并解決保密性、特權(quán)和數(shù)據(jù)安全問題。定期風(fēng)險評估對于識別漏洞至關(guān)重要，而正式的審批流程則確保只使用安全、合規(guī)的人工智能平臺。
 
Technical controls are critical. Data classification systems should identify sensitive information before it is processed by AI tools. Access controls, such as role-based permissions and monitoring, can prevent unauthorised use of consumer AI platforms. An approved list of enterprise-grade AI tools, designed with legal and compliance requirements in mind, ensures efficiency without sacrificing security. These tools must integrate with existing cybersecurity infrastructure and incorporate data loss prevention measures to protect sensitive information.
技術(shù)控制至關(guān)重要。數(shù)據(jù)分類系統(tǒng)應(yīng)在敏感信息被人工智能工具處理之前對其進行識別。訪問控制（如基于角色的權(quán)限和監(jiān)控）可以防止消費者人工智能平臺被未授權(quán)使用。一份經(jīng)過批準(zhǔn)的企業(yè)級人工智能工具清單，在設(shè)計時考慮了法律和合規(guī)要求，能夠在不犧牲安全性的前提下確保效率。這些工具必須與現(xiàn)有的網(wǎng)絡(luò)安全基礎(chǔ)設(shè)施相集成，并納入數(shù)據(jù)防泄漏措施，以保護敏感信息。
 
Training and awareness underpin effective governance. Mandatory training for all legal staff, from partners to associates, should cover the technical and legal risks of AI, including UK GDPR obligations and SRA requirements. Regular updates on emerging threats, such as new data breach tactics or regulatory changes, keep teams informed. Clear reporting mechanisms for AI-related incidents foster transparency and enable swift responses to potential breaches, minimising damage.
培訓(xùn)和意識是有效治理的基礎(chǔ)。所有法律人員（從合伙人到律師助理）都必須接受培訓(xùn)，內(nèi)容應(yīng)涵蓋人工智能的技術(shù)和法律風(fēng)險，包括英國通用數(shù)據(jù)保護條例的義務(wù)和律師監(jiān)管局的要求。定期更新新出現(xiàn)的威脅（如新的數(shù)據(jù)泄露策略或法規(guī)變化），能讓團隊了解相關(guān)情況。明確的人工智能相關(guān)事件報告機制有助于提高透明度，并能對潛在的違規(guī)行為做出快速反應(yīng)，從而將損害降到最低。
 
Practical Recommendations for Legal Teams
給法律團隊的實用建議
 
Legal teams must act swiftly to address AI data risks, with immediate, medium-term, and long-term strategies. In the short term, conducting a Shadow AI audit is essential to uncover unapproved tool usage. This involves surveying staff to identify all AI tools in use, assessing the data being processed, and documenting potential exposures. This could be backed up by technical solutions, such as an “AI Gateway” to help enforce these policies by automatically detecting and blocking sensitive client data from reaching unauthorised AI platforms, providing real-time protection while policies are developed. Emergency controls, such as blocking access to consumer AI platforms and providing approved alternatives, can halt further risks. Clear communication ensures staff understand the urgency and comply with new protocols.
法律團隊必須迅速采取行動，應(yīng)對人工智能數(shù)據(jù)風(fēng)險，并制定即時、中期和長期策略。短期內(nèi)，開展“影子人工智能”審計至關(guān)重要，以發(fā)現(xiàn)未經(jīng)批準(zhǔn)的工具使用情況。這包括調(diào)查員工，確定所有正在使用的人工智能工具，評估正在處理的數(shù)據(jù)，并記錄潛在的風(fēng)險暴露點。這可以通過技術(shù)解決方案來支持，例如“人工智能網(wǎng)關(guān)”，通過自動檢測和阻止敏感客戶數(shù)據(jù)流向未授權(quán)的人工智能平臺來幫助執(zhí)行這些政策，在政策制定過程中提供實時保護。緊急控制措施，如阻止訪問消費者人工智能平臺并提供經(jīng)批準(zhǔn)的替代方案，可以遏制進一步的風(fēng)險。清晰的溝通能確保員工理解事情的緊迫性并遵守新協(xié)議。
 
In the medium term, comprehensive AI policies should align with UK GDPR, SRA, and FCA requirements. Again technical controls, not just documentation, could be used to apply sensitive data classification, access controls and audit trails, regardless of which AI tool employees attempt to use. Vendor vetting procedures are crucial, ensuring AI providers meet stringent security and compliance standards, with contracts that protect client data and include audit rights. An AI-specific incident response plan prepares teams to act decisively in case of a breach, minimising regulatory and reputational fallout.
從中期來看，全面的人工智能政策應(yīng)符合英國《通用數(shù)據(jù)保護條例》、律師監(jiān)管局（SRA）和金融行為監(jiān)管局（FCA）的要求。同樣，無論員工嘗試使用哪種人工智能工具，都可以采用技術(shù)控制（而非僅僅依靠文件記錄）來實施敏感數(shù)據(jù)分類、訪問控制和審計跟蹤。供應(yīng)商審查程序至關(guān)重要，要確保人工智能提供商符合嚴(yán)格的安全和合規(guī)標(biāo)準(zhǔn)，同時簽訂的合同需保護客戶數(shù)據(jù)并包含審計權(quán)。專門針對人工智能的事件響應(yīng)計劃能讓團隊在發(fā)生數(shù)據(jù)泄露時果斷采取行動，將監(jiān)管風(fēng)險和聲譽損失降至最低。
 
For the long term, investing in enterprise-grade AI solutions designed for legal work, such an AI Gateway described above, is vital. Annual policy reviews keep governance measures aligned with evolving technology and regulations, embedding AI governance into the broader compliance strategy to maintain client trust while leveraging AI’s benefits.
從長遠(yuǎn)來看，投資專為法律工作設(shè)計的企業(yè)級人工智能解決方案（例如上述的人工智能網(wǎng)關(guān)）至關(guān)重要。年度政策審查使治理措施與不斷發(fā)展的技術(shù)和法規(guī)保持一致，將人工智能治理融入更廣泛的合規(guī)戰(zhàn)略中，從而在利用人工智能優(yōu)勢的同時維護客戶信任。
 
Future Outlook and Conclusion 未來展望與結(jié)論
 
The UK’s regulatory landscape is evolving rapidly, with the proposed AI Bill, UK GDPR, and NIS2 Directive signalling heightened scrutiny of AI governance. Legal departments that fail to act risk becoming cautionary tales, facing fines, client loss, and reputational damage. Conversely, those that implement robust governance can gain a competitive edge, demonstrating to clients their commitment to security and compliance while harnessing AI’s efficiency.
英國的監(jiān)管環(huán)境正在迅速演變，擬議的《人工智能法案》、英國《通用數(shù)據(jù)保護條例》和《網(wǎng)絡(luò)與信息系統(tǒng)安全指令2》表明對人工智能治理的審查將更加嚴(yán)格。未能采取行動的法律部門可能會成為警示案例，面臨罰款、客戶流失和聲譽損害。相反，那些實施健全治理的部門可以獲得競爭優(yōu)勢，向客戶展示其對安全和合規(guī)的承諾，同時利用人工智能的效率。
 
The urgency of addressing AI data leaks is undeniable. Legal teams must act now to audit AI usage, implement controls, and educate staff. By balancing innovation with risk management, UK organisations can protect sensitive data, uphold client trust, and navigate a complex regulatory landscape. The legal profession is built on trust and diligence. In the AI era, these principles demand proactive governance to ensure technology serves as a tool for progress, not a source of peril.
解決人工智能數(shù)據(jù)泄露問題的緊迫性毋庸置疑。法律團隊必須立即采取行動，審核人工智能的使用情況，實施管控措施，并對員工進行培訓(xùn)。通過在創(chuàng)新與風(fēng)險管理之間取得平衡，英國的組織能夠保護敏感數(shù)據(jù)，維護客戶信任，并應(yīng)對復(fù)雜的監(jiān)管環(huán)境。法律行業(yè)建立在信任和勤勉的基礎(chǔ)之上。在人工智能時代，這些原則要求我們進行前瞻性治理，以確保技術(shù)成為推動進步的工具，而非危險的源頭。
 
Author :Camilo Artiga-Purcell serves as General Counsel at Kiteworks, where he leads legal strategy and governance initiatives for secure content communications and collaboration. With extensive experience in data privacy, cybersecurity, and emerging technology law, he advises organizations on managing AI-related risks while maintaining competitive advantage.
作者：卡米洛·阿蒂加-珀塞爾擔(dān)任Kiteworks的總法律顧問，負(fù)責(zé)領(lǐng)導(dǎo)安全內(nèi)容通信和協(xié)作方面的法律戰(zhàn)略及治理計劃。憑借在數(shù)據(jù)隱私、網(wǎng)絡(luò)安全和新興技術(shù)法方面的豐富經(jīng)驗，他為各組織提供有關(guān)管理人工智能相關(guān)風(fēng)險同時保持競爭優(yōu)勢的建議。