Despite the continuous and shocking gender disparity in cybersecurity where 16% of Fortune 500 CISOs are women, women continue to contribute, develop, and lead amazing careers. As has now become a Forrester Security & Risk Summit tradition, a room full of amazing women and a few brave fellas gathered last week as part of Forrester Women’s Leadership Program to celebrate the successes, and solve for the many challenges women face in this field.  The theme? “To Propel You Career In Security & Risk, Choose Your Advisers And Nuggets Of Advice Wisely.”   We asked the attendees to share some of the best and worst advice they had received over their careers. What resulted was an inspiring, interactive, and thought-provoking session that dissected the following:   Careers are a winding road, both studded with obstacles and made smoother by mentors with sound advice. Laura Koetzle moderated a panel of three highly accomplished senior women: Judith Conklin, CIO at the US Library of Congress, Faye Dixon-Harris, Managing Director at the Federal Home Loan Bank San Francisco, and Tameika Turner, Senior Cybersecurity Program Manager at the National Nuclear Security Administration. Each woman shared that she didn’t set out to build her career in cybersecurity or technology but rather arrived in the field via the US military, an entry-level role at a financial advising firm, or an administrative job in government while studying. All three women also received pivotal advice and sponsorship from mentors to: “think bigger” when she expressed the ambition to be a Deputy (rather than the C-level executive); go back to school so that no-one would ever be able to use a lack of a degree as an excuse to deny a promotion; and move to a new organization with mentor take on a first technical role.  You need to sift the advice that bombards you from all directions to separate the gold from the muck. At all stages in your career, people will come to you with well-intentioned advice. But, discerning between what is useful and what is not can be a challenge. Sift your advice by asking questions about the advice and who is giving it such as: who are you, and why are you telling me this? Do you have a vested interest in giving me this advice? Will what works for you work for me? Identify green flag advice from those who have your best interest at heart, listened to your perspective, and bring in a new perspective. Beige flag advice can be beneficial – these are pieces of advice that can be helpful, if applied in the right circumstances or context.   Good and bad advice comes in abundance, and in themes. For over a decade, women have been told to Lean In, until we all discovered that if we leaned in any further we’d snap. This is not the only well-intended, yet bad advice we’ve received. Attendees shared the multitude of good, and bad advice they’ve received, and it turns out that there are universal themes that we have almost all experienced (see the figure below). We have been told that we’re not ready, to be normal, to be ‘less,’ and to talk things out when we clearly shouldn’t. And thankfully, many of us have also been the lucky recipients of advice about how to hold boundaries, to brag about our achievements, to not hold back, and to assume positive intent in others.   Well-intentioned advice especially on genAI, leadership, burnout, skills and certification, and networking. As analysts, we spend a large chunk of our time debunking the status quo – the well-intentioned advice given to security leaders. Attendees joined analysts in debunking myths in the following subject matters:  You cannot, and should not always meditate your way out of burnout in cybersecurity. Instead, address systemic issues that cause burnout, and be aware of the imbalance between expectations, resources, and perceptions that lead to burnout.   You cannot continue to blindly experiment with genAI for genAI’s sake! Instead, focus on the benefits that genAI has delivered to you so far – and we mean you as an individual in your profession – to leverage the tech more strategically moving forward.  Don’t hold yourself back from a desired next step in your career because you don’t quite feel ready — you’re never going to feel 100% ready. Instead, every year, identify your strong points and areas to improve, find people who excel in your areas of improvement and learn from them, and prioritize hiring people who are strong in those areas who you can rely on and learn from.   You cannot expect specific degrees or certifications to magically get you hired or promoted. Instead – and in addition to these still widely required but flawed indicators of competence – pursue the experience and relationships that will propel your career forward. Seek meaningful mentors, get hands-on low-cost training or free cybersecurity skills and training platforms, and link your diverse experience and background to the value you bring to the role.   You don’t have to learn to play golf to build a network. Instead look for opportunities within your organization such as community service days, affinity group, or virtual water cooler chats, to foster relationships. Attending industry events or conferences presents a chance to make new acquaintances. LinkedIn is a great way to maintain contact with your network, and request introductions to others from your existing connections.   Asking women and other minority groups to solve systemic bias problems that they did not create causes high stress levels, compounds feelings of difference, and leads to spending less time on career-related activities. Do not underestimate the power of taking the time to share and learn from others. If this year’s Security & Risk Summit Forrester Women’s Leadership Program reminded us of anything, it is of the power of community, vulnerability, and sharing can lift us all.    This blog, and the Forrester Women Leadership session, benefited from Research Associate, Chiara Bragato’s input.   source

Every business is unique in its approach to technology modernization. Likewise, the reasons behind initiating a modernization strategy will vary from business to business. However, a few key drivers have emerged as a common thread between businesses. In a recent Rocket Software survey, commissioned by Forrester Consulting, the top three drivers for IT modernization were improving IT reliability and resilience (53%), improving IT capabilities to enhance stakeholders’ experience (51%), and improving access to insights to unlock the value of data (48%). Whatever the reason, a modernization journey is a serious endeavor that can take up considerable effort and time. Rocket Software’s survey found that 74% of respondents have been on their IT modernization journey for more than a year. As they look to move their modernization initiatives forward, IT professionals shouldn’t feel like they need to take five steps backward or skip five steps forward to make a modernization solution work for them. Whether it’s a cloud migration, existing mainframe systems, or a hybrid environment, successful modernization requires a technology partner capable of meeting you wherever you are on that journey. Let’s examine what this looks like across organizations and how the right partner can transform the modernization journey. Navigating modernization in any environment The path toward modernization itself can take shape in a number of different ways. For example, one organization might decide they need to be heavily invested in cloud infrastructure to be able to scale up or down quickly, while another may have mission-critical systems tied to the mainframe that necessitate a hybrid strategy. Whatever the environment looks like, IT decision-makers must weigh the benefits of each destination and move quickly. The mainframe houses some of the most sensitive, yet critical, historical data for organizations, and is estimated to be responsible for processing nearly 70% of enterprise workloads. So many organizations choose to rely on mainframe systems because of those workloads and their ability to handle a high volume of transactional data. On top of that, mainframe systems also keep that data secure and governed in a single setting versus a multitude of environments. Cloud, on the other hand, brings its own advantages to the modernization journey. Bringing data into a cloud environment can make it easier to tap into the full power of AI and analytical models, generating deeper business insights and uncovering new market opportunities. The road to modernization isn’t a binary choice between cloud and mainframe systems though. Adopting a hybrid approach is also a popular strategy, giving businesses the perfect blend of both. In this setting, organizations can opt to modernize in place, preserving existing systems and processes that have taken years to establish while ensuring the treasure trove of historical data that lives on the mainframe can be securely leveraged for other initiatives. Whatever the approach, each business needs a partner that can support its unique journey. Mapping out the modernization journey Every modernization journey plays out differently. Regardless, businesses must get their approach right. Nearly half (44%) of survey respondents stated that IT modernization challenges have led to delayed timelines and one-third said that these problems resulted in reduced productivity. So, how can an organization know whether a technology partner is equipped to meet their needs or not? The right partner will deliver solutions that add value to existing systems while enabling modernization initiatives to thrive. With a trusted modernization partner, these organizations can tap into the tools to stay competitive by leveraging their data, applications, and infrastructure. For instance, Rocket Software has extensive solutions that will help clients to modernize the existing mainframes in place.  We also have solutions like Rocket Software’s Rocket® Enterprise Suite and Rocket® COBOL that enable enterprises to run business applications written in COBOL and PL/I coding languages in the cloud. We also help enterprises that need to run hybrid, with some workloads in place on the mainframe, while others will run on the cloud. This expands the capabilities of organizations by adding a new dimension to the ability to run workflows and distribute data wherever they prefer and empowers the mobile workforce to work from anywhere. Whichever path they choose, and wherever they are on their journey, mainframe modernization solutions can take enterprises to the next level, preparing them for the technologies of tomorrow. These solutions allow organizations to update applications and architecture incrementally, rather than making sudden and expensive replacements. These solutions will meet enterprises on whichever road they choose to go down. Learn more about mainframe modernization with Rocket Software source

On Dec. 17, Salesforce unveiled Agentforce 2.0, the latest iteration of its AI-driven platform. It is designed to integrate a customizable “team” of semi-autonomous AI agents into enterprise workflows. Agentforce 2.0 includes enhanced reasoning and data retrieval capabilities that enable AI to respond to complex questions. Salesforce describes Agentforce 2.0 as “the digital labor platform for enterprises,” offering a “limitless” workforce powered by AI agents. These agents can be deployed across any department, equipped with a new library of pre-built skills, and can take action across systems or workflows. “Humans with agents drive customer success together,” Salesforce CEO Marc Benioff said during a livestreamed presentation. Agentforce 2.0 will be released in full in February 2025. However, users can begin deploying Agentforce 2.0 in Slack in January. What’s new about Agentforce 2.0? Through Agentforce 2.0, users can pull from a library of agent “skills” — AI agents designed for specific tasks — or build their own using natural language prompts. These agents can execute multi-step plans and follow “if/then” instructions, offering flexibility across various workflows. The Skills Library suggests linked actions based on an agent’s role. Image: Salesforce Pre-built skills and workflow integrations allow for more effective customization. Agents built through Agentforce can seamlessly connect to tools like CRM, Slack, Tableau, Mulesoft, and Salesforce’s AppExchange. New, pre-built skills include: Sales Development. Sales Coaching. Marketing Campaign. Commerce Merchant. In Tableau, Skills for Analytics and Insight provide visualizations to track and analyze how well agents perform. Slack integration is another key highlight. With Slack Actions, users can automate updates by setting an agent to send project updates via direct message. Some of these features are already available: Tableau Semantic Layer, Skills for Sales Development, and Skills for Sales Coaching are available now. The latter two add $2 per conversation to your Salesforce bill. Skills for Tableau will launch on Dec. 18. Agent Builder provides a solution for customers who don’t find what they need among the pre-built agents. Using natural language prompts, users can design their own agents. Salesforce offered “’Onboard New Product Managers” as an example of a custom prompt. Agent Builder will also include pre-built Slack actions. How does Agentforce 2.0 integrate with Slack? In Slack, employees can message or mention the Agentforce agents just as they would a coworker. Users will also find a new Agentforce Hub within Slack. In this example, an agent designated as an “Account Insights Expert” answers a prompt with Slack. Image: Salesforce Generative AI enables Slack Enterprise Search, a new way to consider context from Slack DMs, channels, and canvases. Agentforce in Slack, Slack Actions in Agent Builder, and Slack Enterprise Search will all be available to the general public in January. SEE: Organizations need to understand the differences between types of AI to avoid unnecessary expenses — and sometimes, the job doesn’t call for generative AI. More must-read AI coverage How does the Atlas Reasoning Engine enhance Agentforce 2.0? “Reasoning” is the next frontier in generative AI, with models like Amazon Nova performing complex operations slightly more slowly than their peers to generate deeper answers. Salesforce’s contribution is advanced reasoning and retrieval-augmented generation in Atlas Reasoning Engine, the model behind Agentforce. Salesforce’s example of a question that might benefit from advanced reasoning was, “What would be the right investment vehicle for my child’s college fund based on my current income and risk preferences?” With RAG, Agentforce can pull unstructured metadata from elsewhere in the Salesforce Platform. Essentially, RAG checks the AI’s work. “Unity Environmental University is leveraging Salesforce’s Agentforce to expand our support beyond routine inquiries, allowing our employees to focus on learners who need more personalized guidance,” wrote Melik Khoury, president and CEO of Unity Environmental University, in a press release from Salesforce. “By integrating agentic AI into our workflows, we can quickly address standard questions like financial aid details or class registration while freeing our team to engage more deeply with students.” Enhanced reasoning and RAG in the Salesforce Platform will be open for business in February. Salesforce goes all-in on AI Salesforce has committed significantly to integrating generative AI into its products, including the enthusiasm for Agentforce that kicked off in September. “In the next few years, I’d expect we’ll be able to deliver more agentic experts,” Claire Cheng, VP of machine learning and engineering for Salesforce AI, said in a press release.”The reasoning engine should be one of the first factors enterprise organizations consider when comparing digital labor options.” source

Every year, millions of businesses are created around the world. In order for these big ideas to turn into successful startups, most of them will inevitably come up against the challenges of fundraising.  While there is no magic formula, there are variables that founders can hone in on when engaging with potential investors. TNW sat down with San Francisco-based VC Plug and Play early-stage investor Letizia Royo-Villanova during the Red Bull Basement global final in Tokyo to get her insights.  The one thing that really needs to stand out, according to Royo-Villanova, is the drive and authenticity of the founder. “Maybe they’ve experienced a problem, or know someone that has experienced that problem, and so they really want to solve it. Not because of making money — of course that’s a plus — but because they actually care about solving that problem.” In addition to said passion, the ability to sell is another key skill. Founders are constantly required to sell their ideas to investors, to clients — and also to talent. “The best founders will have the best talent in their team,” Royo-Villanova states.  How Startup Amsterdam Boosts Innovation and Growth at TNW Conference Discover how the City of Amsterdam partnered with TNW to amplify its startup ecosystem, attract global talent, and foster innovation that drives economic impact. While direct industry experience is valuable, it’s not always essential. “There are great entrepreneurs out there that don’t necessarily have that experience. They are kind of born with that drive of founding a company.”  However, having insight into the customer and understanding the market are non-negotiables: “You really need to understand the pain point and the industry. That is going to facilitate a lot of doors opening in the future,” the VC adds.   Lastly, personality and rapport matter. “I do think that you feel it in the first half hour,” Royo-Villanova says, referring to understanding whether a founder is someone the VC is going to want to spend time with. “If you end up investing in a founder, you are going to have a lot of meetings with that person. So if you don’t feel the vibe, you don’t want to invest in them.” Mistakes founders make when pitching Even though a founder may have the best idea imaginable, creating an impactful pitch is essential in order to get investors on board. (Not everyone has the good fortune to survive a disastrous pitch like the one Nvidia co-founder Jensen Huang famously gave Don Valentine of Sequoia in 1993.)  One of the most common mistakes Royo-Villanova sees is founders spending too much time on describing the general problem as opposed to focusing on their specific solution. “If it’s a climate or sustainability startup,” the VC explains, “and they spend 15 minutes talking about how there’s a climate issue, I don’t need to hear that. They could tell me in one or two sentences. Then we can concentrate on more important things.” And while solo entrepreneurs may well succeed, the VC is more likely to consider funding a founder team of two or more. “Building a startup is hard enough, and if you do it by yourself, what if you suddenly have a bad week or a bad month? You need that other person to hold you up,” she says. Furthermore, teams with complementary skills are more likely to drive success in the future.  Common pitfalls when running an early-stage startup Of course, beyond the pitch, there is also the small matter of actually running the business. Specifically, when it comes to fundraising, Royo-Villanova believes that a major misstep is taking money from any available investor without considering strategic alignment.  “The money is going to run out, but the support from the people that invest in you shouldn’t,” she says. The right VC can offer help with recruitment, sales, or industry network connections. Pivoting back to the question of talent, hiring decisions is a critical area when it comes to running the business. Founders often try to save money by hiring cheaper talent, but Royo-Villanova says this can backfire further down the road. “It’s about finding the right fit for your company and building a culture from day one,” she says. Finally, an inability to pivot is another potentially fatal flaw. “If you have an idea, talk to potential customers from day one, understand if this is something that is actually a problem and that they are going to prioritise and that they are going to pay for and if not, it’s ok to pivot. If you’re going to fail, fail fast — and it’s not even failing, it’s just changing to something else.” Focus on education and supportive regulation could drive European innovation With all the concerns and recent discourse around the innovation gap between the US and Europe, we could not help but ask the California-based VC what she feels are the most significant areas holding Europe back.  One of the main issues she identifies as a lack of early exposure to innovation and entrepreneurship. “I don’t feel I was aware of the world of innovation or venture capital as much as probably some students in the US,” Royo-Villanova (who hails from Spain) says. “If you start from a very young age to introduce that culture of innovation and explain how important it is, it’s going to help a lot in the future.” Regulation and corporate attitudes also play a role. European corporations can often exhibit a risk-averse mindset, in contrast with a more dynamic and entrepreneurial culture from their North American counterparts. Moreover, complex regulatory frameworks can stifle startups from scaling quickly — something initiatives such as the recently launched EU Inc hope to overcome.  Founders seeking to build successful startups need to embody passion and an ability to sell, as well as customer insight, while avoiding common pitfalls including neglecting strategic fundraising and failing to pivot quickly. Meanwhile, Europe’s innovation ecosystem would benefit from early education, a shift in corporate attitudes, and streamlining regulations.  Addressing all these challenges

A virtual private network (VPN) is a marvelous tool for protecting people and their data while browsing the internet, especially when working from unsecured or weakly protected networks like those at public libraries and coffee shops. From a business perspective, VPNs keep business data secure when employees work with sensitive material like trade secrets and proprietary information. VPN tunnels are also instrumental, as they provide users with an encrypted connection between their device and the internet. However, given the enriched data flow and volumetric information brought on by VPNs, you and/or your IT team should still monitor them regularly. The technical feedback you can gather by doing so will help you finetune and configure your VPN connections for optimal performance. As an alternative to VPNs, SD-WAN (Software-Defined Wide Area Network) offers businesses many more use cases. For instance, organizations that lean heavily on Voice over Internet Protocol (VoIP) phone services can use it to simplify enterprise-scale network management. 1 RingCentral RingEx Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Medium (250-999 Employees), Large (1,000-4,999 Employees), Enterprise (5,000+ Employees) Medium, Large, Enterprise Features Hosted PBX, Managed PBX, Remote User Ability, and more 2 Talkroute Employees per Company Size Micro (0-49), Small (50-249), Medium (250-999), Large (1,000-4,999), Enterprise (5,000+) Any Company Size Any Company Size Features Call Management/Monitoring, Call Routing, Mobile Capabilities, and more What problems does SD-WAN solve? I’m assuming most people interested in this post are comfortable with networking basics, like WAN (Wide Area Network) that spans a large geographic area, connecting multiple local networks (LANs) across cities, countries, or even continents. So we’re going to skip the basics. If not, check out this guide on essential networking fundamentals before continuing on. SD-WAN represents a logical progression from traditional WAN, providing benefits like dynamic traffic management with centralized control. It allows users to deploy different connection types interchangeably by using software to abstract the network layer. The fundamental benefit of SD-WAN over traditional WAN is its ability to intelligently route traffic across multiple connection types, optimizing performance, reducing costs, and providing greater flexibility and scalability. SD-WAN offers businesses improved network performance, cost savings, enhanced security, and greater agility by enabling dynamic, intelligent traffic routing across diverse connection types, making it a more scalable and flexible solution compared to traditional VPNs. Let’s walk through why Traffic optimization and improved network efficiency A key advantage of SD-WAN is its ability to avoid vendor lock-in by using a virtualized architecture, allowing businesses to combine various transport services. Unlike traditional network infrastructure, which is often rigid and hardware-dependent, the best SD-WAN vendors give organizations the flexibility to optimize bandwidth across multiple connection types, such as broadband, mobile, Wi-Fi, and satellite. This flexibility enables network administrators to prioritize critical traffic more effectively, reduce reliance on centralized data centers by eliminating backhauling, and create more efficient, direct routing paths to improve overall network performance. Providing cost-effective solutions Even though WAN connectivity has been around for a while, one of its peskiest challenges has always been figuring out how to connect widely dispersed data centers in a cost-effective manner. Technologies like MPLS (Multiprotocol Label Switching), for instance, provided a respite — especially for organizations operating in rugged environments—but MPLS often brings a huge cost disadvantage. SEE: Discover other reasons to avoid MPLS and better alternatives.  SD-WAN, however, is more practical (to deploy) and much less expensive than MPLS because it doesn’t require specialized equipment to conduct routing over the internet. Another cost-effective aspect of using SD-WAN is its ability to aggregate multiple, less expensive internet connections (such as broadband, LTE, and Wi-Fi) to create a more reliable and efficient network. This reduces the need for expensive, dedicated leased lines or private WAN circuits, allowing businesses to use more affordable and flexible transport services while maintaining high performance. Increased control through application-level visibility Nothing jams up the efforts of network administrators and cybersecurity professionals more than a lack of control over their organization’s online traffic. That said, the application-level visibility provided by SD-WAN allows you to control traffic more effectively. For instance, SD-WAN allows administrators to fine-tune Quality of Service (QoS) by prioritizing VoIP traffic over less critical data, ensuring consistent call quality. With real-time monitoring and dynamic traffic routing, SD-WAN can adjust network paths to avoid congestion, and ensure optimal performance for VoIP applications even during peak usage times. Centralized management SD-WAN is the network tool of choice for enterprises with multiple office branches that want to maintain a centralized oversight. In general, a ton of network administrators face the challenge of having to orchestrate a gauntlet of deployed devices and endpoints, so SD-WAN is a logical choice because it makes networks more manageable and cost-effective. With centralized management to handle data packets and workflows between branches, network operations are simplified company-wide. Cybersecurity administration Because of its centralized network management, SD-WAN allows you to deploy uniform security measures including dynamic encryption tunnels, IP security (IPsec), and next-generation firewalls (NGFW) to ensure that all traffic is protected. Additionally, SD-WAN offers advanced features like network segmentation, which isolates critical parts of the network to reduce risk, and intrusion protection to detect and block potential threats. These built-in security features work together to provide end-to-end encryption, making SD-WAN a powerful solution for defending against network security threats, especially in environments with remote or distributed teams. By simplifying the process of managing network security, SD-WAN makes it easier for IT teams to protect sensitive data and maintain compliance with industry regulations. What Problems Does a VPN Solve? A VPN safeguards online activity by providing a measure of intrusion protection against unauthorized third parties and other rogue actors. They use encrypted data transmission to prevent the intercepting and eavesdropping of connections that can occur via packet sniffing and other snooping tactics. The best enterprise VPN services achieve this protection through VPN tunneling, which creates an encrypted connection between the user’s device and the endpoint or remote server they are accessing. If you are potentially in

Finnish startup Oura has closed its Series D funding round at $200mn, bringing the smart ring maker’s valuation to a cosy $5.2bn. Oura’s smart ring uses 20 biometric markers to track sleep, physical activity, and stress resilience. The device displays this data on an app that gives you a personalised “readiness” score. We tested the wearable earlier this year and were genuinely impressed.  Founded in 2013, Oura secured its first funding on Kickstarter, the crowdfunding site, in 2016. Counting this new tranche of capital, the tech startup has raised $550mn since inception. “We’ve made significant progress in advancing our mission to make health a daily practice and will use this funding to unlock new opportunities, with AI development at the centre of our strategy,” said Tom Hale, Oura’s CEO. In 2022, Oura became a unicorn and sold its millionth ring. Two years on, the company claims to have recently sold its 2.5 millionth device and to have made $500mn in sales this year alone.    How Startup Amsterdam Boosts Innovation and Growth at TNW Conference Discover how the City of Amsterdam partnered with TNW to amplify its startup ecosystem, attract global talent, and foster innovation that drives economic impact. “We know that Oura has the potential to change lives at scale, and we’re excited to continue leading the market in innovation while pursuing opportunities that extend beyond the ring,” said Hale. Oura said it signed partnerships with key retailers such as Amazon and Target this year. The ring is especially popular with celebrities — including Prince Harry, Gwyneth Paltrow, and Jennifer Aniston. Even the Pentagon made a $96mn order in October to put the devices in (or should I say “on”) the hands of soldiers. While sales of smartwatches flatlined this year, smart rings are surging in popularity. Global smart ring sales are set to almost double from an estimated 1.7mn by the end of 2024 to 3.2mn in 2028, market intelligence firm IDC.  For many users, they’re seen as a more convenient option to smartwatches like the AppleWatch but still contain many of the same features. Smart rings also tend to move less and fit better against the skin.   Smart ring makers sold 880,000 units in 2023, said IDC. The Oura Ring made up 80% of these sales, soaring above competitors like Ultrahuman and Samsung. By those figures, Oura genuinely does seem to be the current lord of the (smart) rings. Sorry, I couldn’t help myself.  ​​Fidelity Management led the funding round, which also saw the participation of Dexcom, a German provider of glucose monitoring sensors for diabetes patients. source

By Greg Lamm ( December 20, 2024, 8:56 PM EST) — A group of online shoppers said Thursday that Amazon can’t dodge litigation alleging price-gouging during the pandemic, arguing that the retail giant’s efforts to toss the case are contradicted by earlier public statements “trumpeting” the company’s work with Washington’s attorney general to enforce the state’s consumer protection law against price-gougers…. Law360 is on it, so you are, too. A Law360 subscription puts you at the center of fast-moving legal issues, trends and developments so you can act with speed and confidence. Over 200 articles are published daily across more than 60 topics, industries, practice areas and jurisdictions. A Law360 subscription includes features such as Daily newsletters Expert analysis Mobile app Advanced search Judge information Real-time alerts 450K+ searchable archived articles And more! Experience Law360 today with a free 7-day trial. source

AI is a powerful tool for B2B go-to-market (GTM) teams, enabling them to analyze market and customer trends, personalize customer interactions, optimize sales strategies with unprecedented efficiency and accuracy, and more. With all the relentless buzz around AI, it’s easy to start with the tool and then go searching for a way to use it. The problem with that is you’re letting the technology, not your customers and your goals, drive your strategy. Set your company up for AI success by: Starting with clear goals and objectives. For technology to be valuable (i.e., have a measurable impact on the business), it must be acquired for a purpose. Purchasing and implementing AI isn’t a true measure of success. Be sure that you know why you’re onboarding AI. Be clear about what you’re looking for it to enable and what outcome you expect to receive. Any tool purchased without this direction can lead you away from ensuring that your resources and investments are providing value to your customers and your organization. Preparing your data. There’s a reason why sentiments such as “garbage in, garbage out” are a key part of AI conversations. AI is an amplifier. If you put good data into AI with the right direction, it will bring quality results. If you put bad data into AI, it will produce inaccurate insights and flawed outcomes. Investing time and effort into preparing your data for AI is crucial to ensure the accuracy and reliability of its outputs. To mitigate unnecessary risk for your company, also ensure that compliance is a part of the consideration. Educating your teams and leadership. It’s important to not just train your models but to train the resources that will be using the tools as well as your leaders. Technology is only valuable if it’s being used well. A successful AI deployment focuses on educating users so that they’re clear on what it is, how it impacts their work, how they can use it to do their jobs better, and what its limitations are. Being sure that your leadership is well informed on AI is important for driving the technical strategy; fostering AI adoption; helping manage risk; making better use of the insights to make informed decisions; and creating an AI-positive culture of innovation, continuous learning, and openness to change. Experimenting with pilots. We’ve all had experiences rolling out tech and then it doesn’t quite behave the way we thought it would. This can be very disruptive with large rollouts. It’s best practice for onboarding any technology (especially AI) to start with experiments and pilots, measure results, discover what works and what doesn’t, and optimize the tool and process before rolling it out broadly. Setting clear governance and guidelines. AI can introduce scenarios that require updates to corporate governance and policies. Work with your IT, data, and legal teams to ensure that governance policies are updated to account for these new scenarios and that the guidance is communicated and understood. Focus on areas such as AI ethics (making AI free from bias and aligning it with your company values), appropriate data access, and internal and external transparency regarding your AI usage. B2B GTM teams have a lot to consider before successfully selecting and onboarding AI, so let’s continue the conversation. Contact your Forrester account manager to set up a guidance session with me, and join me at B2B Summit North America, March 31 to April 3 in Phoenix, where I’ll be talking about how you can build trust with AI — for your company and your customers. source

The European Data Protection Board has published an opinion addressing data protection in AI models. It covers assessing AI anonymity, the legal basis for processing data, and mitigation measures for impacts on data subjects for tech companies operating in the bloc. It was published in response to a request from Ireland’s Data Protection Commission, the lead supervisory authority under the GDPR for many multinationals. What were the key points of the guidance? The DPC sought more information about: When and how can an AI model be considered “anonymous” — those that are very unlikely to identify individuals whose data was used in its creation, and therefore is exempt from privacy laws. When companies can say they have a “legitimate interest” in processing individuals’ data for AI models and, therefore, don’t need to seek their consent. The consequences of the unlawful processing of personal data in the development phase of an AI model. EDPB Chair Anu Talus said in a press release: “AI technologies may bring many opportunities and benefits to different industries and areas of life. We need to ensure these innovations are done ethically, safely, and in a way that benefits everyone. “The EDPB wants to support responsible AI innovation by ensuring personal data are protected and in full respect of the General Data Protection Regulation.” When an AI model can be considered ‘anonymous’ An AI model can be considered anonymous if the chance that personal data used for training will be traced back to any individual — either directly or indirectly, as through a prompt — is deemed “insignificant.” Anonymity is assessed by supervisory authorities on a “case-by-case” basis and “a thorough evaluation of the likelihood of identification” is required. However, the opinion does provide a list of ways that model developers might demonstrate anonymity, including: Taking steps during source selection to avoid or limit the collection of personal data, such as excluding irrelevant or inappropriate sources. Implementing strong technical measures to prevent re-identification. Ensuring data is sufficiently anonymised. Applying data minimisation techniques to avoid unnecessary personal data. Regularly assessing the risks of re-identification through testing and audits. Kathryn Wynn, a data protection lawyer from Pinsent Masons, said that these requirements would make it difficult for AI companies to claim anonymity. “The potential harm to the privacy of the person whose data is being used to train the AI model could, depending on the circumstances, be relatively minimal and may be further reduced through security and pseudonymisation measures,” she said in a company article. “However, the way in which the EDPB is interpreting the law would require organisations to meet burdensome, and in some cases impractical, compliance obligations around purpose limitation and transparency, in particular.” More must-read AI coverage When AI companies can process personal data without the individuals’ consent The EDPB opinion outlines that AI companies can process personal data without consent under the “legitimate interest” basis if they can demonstrate that their interest, such as improving models or services, outweigh the individual’s rights and freedoms. This is particularly important to tech firms, as seeking consent for the vast amounts of data used to train models is neither trivial nor economically viable. But to qualify, companies will need to pass these three tests: Legitimacy test: A lawful, legitimate reason for processing personal data must be identified. Necessity test: The data processing must be necessary for purpose. There can be no other alternative, less intrusive ways of achieving the company’s goal, and the amount of data processed must be proportionate. Balancing test: The legitimate interest in the data processing must outweigh the impact on individuals’ rights and freedoms. This takes into account whether individuals would reasonably expect their data to be processed in this way, such as if they made it publicly available or have a relationship with the company. Even if a company fails the balancing test, it may still not be required to gain the data subjects’ consent if they apply mitigating measures to limit the processing’s impact. Such measures include: Technical safeguards: Applying safeguards that reduce security risks, such as encryption. Pseudonymisation: Replacing or removing identifiable information to prevent data from being linked to an individual. Data masking: Substituting real personal data with fake data when actual content is not essential. Mechanisms for data subjects to exercise their rights: Making it easy for individuals to exercise their data rights, such as opting out, requesting erasure, or making claims for data correction. Transparency: Publicly disclosing data processing practices through media campaigns and transparency labels. Web scraping-specific measures: Implementing restrictions to prevent unauthorised personal data scraping, such as offering an opt-out list to data subjects or excluding sensitive data. Technology lawyer Malcolm Dowden of Pinsent Masons said in the company article that the definition of “legitimate interest” has been contentious recently, particularly in the  U.K.’s Data (Use and Access) Bill. “Advocates of AI suggest that data processing in the AI context drives innovation and brings inherent social good and benefits that constitute a ‘legitimate interest’ for data protection law purposes,” he said.  “Opponents believe that view does not account for AI-related risks, such as to privacy, to discrimination or from the potential dissemination of ‘deepfakes’ or disinformation.” Advocates from the charity Privacy International have expressed concerns that AI models like OpenAI’s GPT series might not be properly scrutinised under the three tests because they lack specific reasons for processing personal data. Consequences of unlawfully processing personal data in AI development If a model is developed by processing data in a way that violates GDPR, this will impact how the model will be allowed to operate. The relevant authority evaluates “the circumstances of each individual case” but provides examples of possible considerations: If the same company retains and processes personal data, the lawfulness of both the development and deployment phases must be assessed based on case specifics. If another firm processes personal data during deployment, the EDPB will consider if that firm did an appropriate assessment of the model’s lawfulness beforehand. If the data is anonymised after

Artificial intelligence (AI) makes important decisions that affect our everyday lives. These decisions are implemented by firms and institutions in the name of efficiency. They can help determine who gets into college, who lands a job, who receives medical treatment and who qualifies for government assistance. As AI takes on these roles, there is a growing risk of unfair decisions – or the perception of them by those people affected. For example, in college admissions or hiring, these automated decisions can unintentionally favour certain groups of people or those with certain backgrounds, while equally qualified but underrepresented applicants get overlooked. Or, when used by governments in benefit systems, AI may allocate resources in ways that worsen social inequality, leaving some people with less than they deserve and a sense of unfair treatment. Together with an international team of researchers, we examined how unfair resource distribution – whether handled by AI or a human – influences people’s willingness to act against unfairness. The results have been published in the journal Cognition. With AI becoming more embedded in daily life, governments are stepping in to protect citizens from biased or opaque AI systems. Examples of these efforts include the White House’s AI Bill of Rights, and the European parliament’s AI Act. These reflect a shared concern: people may feel wronged by AI’s decisions. So how does experiencing unfairness from an AI system affect how people treat one another afterwards? AI-induced indifference Our paper in Cognition looked at people’s willingness to act against unfairness after experiencing unfair treatment by an AI. The behaviour we examined applied to subsequent, unrelated interactions by these individuals. A willingness to act in such situations, often called “prosocial punishment,” is seen as crucial for upholding social norms. For example, whistleblowers may report unethical practices despite the risks, or consumers may boycott companies that they believe are acting in harmful ways. People who engage in these acts of prosocial punishment often do so to address injustices that affect others, which helps reinforce community standards. Anggalih Prasetya / Shutterstock We asked this question: could experiencing unfairness from AI, instead of a person, affect people’s willingness to stand up to human wrongdoers later on? For instance, if an AI unfairly assigns a shift or denies a benefit, does it make people less likely to report unethical behaviour by a co-worker afterwards? Across a series of experiments, we found that people treated unfairly by an AI were less likely to punish human wrongdoers afterwards than participants who had been treated unfairly by a human. They showed a kind of desensitisation to others’ bad behaviour. We called this effect AI-induced indifference, to capture the idea that unfair treatment by AI can weaken people’s sense of accountability to others. This makes them less likely to address injustices in their community. Reasons for inaction This may be because people place less blame on AI for unfair treatment, and thus they feel less driven to act against injustice. This effect is consistent even when participants encountered only unfair behaviour by others or both fair and unfair behaviour. To look at whether the relationship we had uncovered was affected by familiarity with AI, we carried out the same experiments again, after the release of ChatGPT in 2022. We got the same results with the later series of tests as we had with the earlier ones. These results suggest that people’s responses to unfairness depend not only on whether they were treated fairly but also on who treated them unfairly – an AI or a human. In short, unfair treatment by an AI system can affect how people respond to each other, making them less attentive to each other’s unfair actions. This highlights AI’s potential ripple effects in human society, extending beyond an individual’s experience of a single unfair decision. When AI systems act unfairly, the consequences extend to future interactions, influencing how people treat each other, even in situations unrelated to AI. We would suggest that developers of AI systems should focus on minimising biases in AI training data to prevent these important spillover effects. Policymakers should also establish standards for transparency, requiring companies to disclose where AI might make unfair decisions. This would help users understand the limitations of AI systems, and how to challenge unfair outcomes. Increased awareness of these effects could also encourage people to stay alert to unfairness, especially after interacting with AI. Feelings of outrage and blame for unfair treatment are essential for spotting injustice and holding wrongdoers accountable. By addressing AI’s unintended social effects, leaders can ensure AI supports rather than undermines the ethical and social standards needed for a society built on justice. Chiara Longoni, Associate Professor, Marketing and Social Science, Bocconi University; Ellie Kyung, Associate Professor, Marketing Division, Babson College, and Luca Cian, Killgallon Ohio Art Professor of Business Administration, Darden School of Business, University of Virginia This article is republished from The Conversation under a Creative Commons license. Read the original article. source