Forrester

Delivering compelling B2B event content is central to an event’s success, but it’s a high-stakes endeavor. Leaders must build organizational alignment around a key theme that aligns to business objectives. They need to use this theme to craft a narrative that feeds into all aspects of event content. They must coordinate with demanding speakers. Resourcing and budgets are stretched. And all of this happens under the pressure of the unmovable deadline of a live event. Forrester research has identified three key challenges that marketers face when it comes to managing their event content: Measuring content effectiveness. Budgets are under pressure, and marketers need to better measure the impact of their event content, but two-thirds of B2B marketers tell us they find this difficult, and many organizations lack the integrated event infrastructure required for effective content measurement. Offering personalized content. Marketers recognize that attendees want (and now expect) higher levels of personalized event content, but they struggle to deliver this. While AI holds the potential to help here, marketers are reluctant (or unable) to fully exploit its capabilities. Driving post-event engagement. Over half of marketers struggle to create content that nurtures attendees post-event and helps to build “community.” Too often, event teams need to shift focus to the next event and lack the bandwidth or internal support to focus here. Take An Audience-Centric Approach To Create Enduring, Impactful Event Content   To overcome these challenges, marketers must take a more disciplined, process-driven approach to their event content strategy and creation. The Forrester Event Content Lifecycle Framework places the target audience at the center of event content planning. It breaks the event content lifecycle into the four key phases of pre-event content planning, pre-event content production, at-event content delivery, and post-event content value realization. For each of these phases, we examine the objectives, inputs, activities, team, and infrastructure that leaders need to consider. Forrester clients can read the report, Master B2B Event Content Best Practices To Drive Engagement, which goes into each of these phases in more detail, and can also request a guidance session to discuss their own event content strategies! source

We are thrilled to announce our research, Deconstructing Human-Element Breaches (Forrester clients can access here), detailing the many and varied risks posed by and to humans — problems that have plagued cybersecurity teams for decades. Forrester clients can use this research as a catalyst for productive conversations with executives and peers across functions about controls to mitigate the human-element breach types most common to their organizations and industries. This blog includes an FAQ based on the most common questions we receive from our clients and the security vendor community about human-element or human-related breaches. Aren’t human-element breaches just social engineering and human error? Whenever we mention human-related breaches, security and risk leaders and practitioners typically think of two main categories: social engineering and human error. This isn’t wrong but isn’t the full picture. After covering these topics separately for years, we decided to deconstruct the problem of human element breaches to uncover what they are and how to address them. This includes a variety of categories such as security culture, social engineering (including phishing), and insider risk. How do I use Forrester’s wheel of human-element breaches? As part of the research, we deconstructed eight breach families containing 25 human-element breach types (see figure below). They include established and emerging attacks such as social engineering, data exfiltration by insiders, and just plain human error. Attackers target humans in so many different ways, and humans behave in such distinct ways that leave them and their organizations vulnerable to attacks. Security leaders can use this wheel to assess the breach types that pose the most risk to their organization, define and describe each breach to stakeholders, and gain buy-in for investment to mitigate these risks. Why do we need this clarity? While it’s great that human-centered security is becoming more top of mind, human-related breaches remain inconsistently defined. For example, well-respected sources, such as the annual Verizon Data Breach Investigations Report, the European Union Agency for Cybersecurity, and the Office of the Australian Information Commissioner’s notifiable data breach reports, each provide different perspectives of what constitutes human-related breaches. This confusion can lead organizations to focus on common breaches while ignoring others, limit the solutions to well-trodden yet ineffective recommendations such as security awareness and training (SA&T), or worse, bury their heads in the sand, overfocusing on technology and not people. Can’t you just train people? After all, this is “just” a human issue. According to Forrester data, 97% of organizations conduct some form of SA&T — hoping for a silver bullet while checking a regulatory compliance box. Despite this, human-related attacks such as business email compromise have quadrupled, CISOs haven’t instilled security cultures in their organizations, training continues to cause friction for learners, and no one knows what behaviors actually change. While awareness of security issues is important, it can never replace the role of technical controls. Even the most vigilant employee will fall for a credible phishing lure or deepfake voice call, accidentally misconfigure an API setting, or send a sensitive file to the wrong recipient. Training is not enough. Technical controls must be in place to protect users from these attacks and change their behavior. If training isn’t as effective as you say it is, can’t we just use tech? While some breaches, such as those caused by human error or social engineering, are easy to associate with people, others that are technologically heavy, such as generative AI (genAI) misuse, are a bit more difficult to understand. Yet it was people relying on fallible genAI content that led the Australian Federal Parliament to publish an inaccurate submission. Without understanding that this is a human-related issue, it is easy to try to rely solely on technology to solve the problem. Security leaders need to strike a balance between training and technical controls. We provide guidance on how to do so using Forrester’s Human-Element Breach Control Matrix. I keep hearing about human risk management, but isn’t it just SA&T 2.0? Far from being SA&T with a fancy new name, human risk management (HRM) solutions present a significant change of mindset, strategy, process, and technology. Forrester defined HRM and began evaluating HRM vendors, encouraging orgs to positively influence security behaviors through evidence-based detection and anticipation of human risk, instead of purely relying on training. Do we really need another tool to manage the human risk? While some technologies in your tech stack provide limited behavioral insights, HRM is unique in that its sole focus is human risk. It integrates with existing tools and technology to measure a vast range of security behaviors and provides a comprehensive view of human risk. HRM also correlates behavioral, threat, access, and knowledge data to surface previously unseen risks. It interacts with people through a set of interventions including training but also through policy updates to protect people in a way that requires minimal effort on their part. Talk To Us Forrester clients can schedule a guidance session or inquiry with: Jinan Budge, for human-centered security, security culture, influence and engagement, and human risk management. Jess Burn, for social engineering and email, messaging, and collaboration security solutions. Joseph Blankenship, for insider risk. Heidi Shey, for data security. Any one of the contributors to this research to discuss the entirety of human-related breaches. source

DeepSeek’s open-source model, DeepThink (R1), by a little-known company in China, sent shock waves across the technology world. It’s amazing. Yes, it does excel at benchmarks comparable to other state-of-the-art models. Yes, it’s partially open source. Yes, the DeepSeek app explains its reasoning by default. But there are far-reaching implications to this important AI development, especially for privacy, security and geopolitical barriers. The Cost Barrier To Training Models Efficiently Just Plummeted What’s disruptive and truly amazing is how the DeepSeek engineers created the DeepThink (R1) model, especially the cost to train the model. Due to clever optimizations, the DeepThink (R1) model purportedly cost around $5.5 million to train. That’s tens of millions of dollars less than comparable models. We expect these optimizations to be copied and improved upon by model builders worldwide. Short term, that is bad news for NVIDIA because it will temper the demand. Longer term, however, the lower cost (and, thus, energy) will open up model creation opportunities for many, many more startups and enterprises alike, thereby increasing demand. This validates the fact that vendors that only provide core AI foundation models won’t be enough, and this disruptive shift will open up the AI model market even more. For tech leaders, this should be a strong signal to closely examine overreliance on a few big players in the AI space. Also, don’t forget that while the cost to train the model has just declined significantly, the cost to support inferencing will still require significant compute (and storage). Don’t cry for NVIDIA and the hyperscalers just yet. Also, there might be an opportunity for Intel to claw its way back to relevance. Intel ceded dominance of high-end computing to NVIDIA, but the company has always bet that tech leaders will want to embed AI everywhere, from the PC to the edge to the data center to the cloud, and there will be strong demand for smaller, targeted large language models (LLMs) — a portfolio of chips at the appropriate price point might just pay off. Edge Computing And Intelligence Is No Longer An Aspiration — It’s Here The DeepSeek app already has millions of downloads on mobile phone app stores. The app connects to and uses the model in the cloud. Another cool way to use DeepSeek, however, is to download the model to any laptop. Several Forrester analysts have run tests on laptops. It’s a bit slow but runnable. This means that the models can run far and wide without the need for specialized hardware. This will dramatically accelerate edge computing. Edge computing processes data closer to its source, reducing latency and bandwidth usage. This helps firms anticipate customer needs, act on their behalf, and operate businesses efficiently in localized contexts, including internet-of-things-enabled scenarios. The ability to run LLMs on laptops and edge devices amplifies these benefits by providing powerful AI capabilities directly at the edge. Based on what we’ve seen so far from DeepSeek R1, it can process and analyze vast amounts of data in real time, enabling more responsive and intelligent edge devices. This capability is particularly valuable in scenarios where immediate decision-making is critical, such as in autonomous vehicles, industrial automation, and smart cities. By leveraging LLMs at the edge, enterprises can achieve faster data processing, improved accuracy in predictions, and enhanced user experiences, all strategic goals of AIOps initiatives. Geopolitical, Privacy, And Security Barriers Remain The massive downloads of DeepSeek mean that thousands (and even millions of users) are experimenting and uploading what could be sensitive information into the app. This may include enterprise data, especially for developers experimenting with the technology. According to its privacy policy, DeepSeek explicitly says it can collect “your text or audio input, prompt, uploaded files, feedback, chat history, or other content” and use it for training purposes. It also states that it can share this information with law enforcement agencies, public authorities, etc., at its discretion. Educate and inform your employees on the ramifications of using this technology and inputting personal and company information into it. Align with product leaders on whether developers should be experimenting with it and whether the product should support its implementation without stricter privacy requirements. There Is No Excuse Not To Pursue AI Innovation (And ROI) Anymore DeepSeek is not just “China’s ChatGPT”; it is a giant leap for global AI innovation, because by reducing the cost, time, and energy to build models, many more researchers and developers can experiment, innovate, and try new sets. Having said that, one should not assume that LLMs are the only path to more sophisticated AI. It may be that a new model architecture brings us right back to needing gobs of compute, especially for artificial general intelligence. But for the time being, DeepSeek’s release of this model and the techniques it used to create it should be a celebratory moment for AI. Now is not the time to scale back on AI prematurely. source

In the high-stakes world of B2B sales, no path to a closed deal is perfectly linear. According to Forrester’s Demand, ABM, And Customer Marketing Survey, 2024, 56% of opportunities handed off to sales fail to close successfully. The preponderant focus that still exists on identifying new opportunities in demand and account-based marketing (ABM) means that revisiting stalled or detoured deals has significant untapped potential. Adaptive programs provide a structured approach to reviving these opportunities, ensuring that they’re not prematurely discarded and that marketing and sales investments yield maximum returns. Why Opportunities Stall, And Why They Shouldn’t Be Abandoned Deals can detour for various reasons, including shifting buyer priorities, budget constraints, or internal changes within the buying organization. When this happens, these opportunities are often deprioritized and fall out of the sales pipeline altogether. But this doesn’t mean they lack potential. Frontline marketing teams can effectively develop strategies to reengage buyers using insights gained during the buying process. Capturing and sharing first-party data — such as updated time frames, missing features, or evolving business needs — ensures that these stalled opportunities remain within the broader B2B Revenue Waterfall™, ready for reactivation when the time is right. Designing Adaptive Programs To Restart Deals That Detour To revitalize stalled deals, organizations must align marketing, sales, and customer-facing teams around shared insights and adopt an adaptable and automated approach to buyer engagement. Adaptive programs leverage standardized reason codes to diagnose why opportunities have stalled and determine the appropriate actions for reengagement. These codes clarify the reasons, context, and next steps for detoured opportunities, ensuring targeted treatments and messaging. Businesses can effectively address setbacks and move opportunities back into the sales pipeline with greater precision and impact by systematically reactivating and recycling deals through tailored demand programs. Accelerating Deals With Program Plays Forrester’s framework emphasizes using specific acceleration program plays to tackle the core reasons for detours. Each play is designed to address a particular buyer concern: Budget. Demonstrate the value of the solution to obtain the necessary budget. Authority. Identify and engage unresponsive or new buying group members and ensure that they align with other buying group members. Need. Showcase how the solution addresses critical business challenges and demonstrate the value of the solution’s functionality. Urgency. To reinforce the importance of timely action, prioritize and create a sense of urgency around the buyer’s challenge ahead of other challenges the business faces. Closing The Gap In B2B Revenue By adopting adaptive programs, businesses can reduce inefficiencies, maximize their marketing ROI, and increase the likelihood of closed deals. Detoured opportunities shouldn’t represent failure but rather an opportunity to refine your approach, leveraging data and insights to meet buyers where they are in their journey. Read the Forrester report, Harnessing Adaptive Programs To Revitalize Deals That Detour, to explore these strategies in greater depth and learn actionable steps for implementation. This comprehensive report provides insights into how adaptive programs can transform stalled deals into successful outcomes. Forrester clients can also schedule a guidance session or inquiry to discuss this important topic for all B2B marketers. source

It’s been some time since Forrester has written about this market, and a lot has changed. Automation is the cornerstone of speed and operational efficiency. With the increasing complexity in IT ecosystems, business applications, and data, the demand for smarter automation is greater than ever. Batch automation and workload automation are certainly not new concepts (they date back to the early days of the mainframe), but they are undergoing a renaissance as organizations optimize their processes. In our upcoming research, we will delve into why it’s time to revisit these technologies, explore the macro trends that impact this market, and how they have the potential to reshape organizations’ automation plans. We’ll help our clients understand the current state of the market, the impact of the latest technological advancements, and new emerging use cases. Why Are We Revisiting This Research? Increased client demand. Enterprises are increasingly demanding insights into the direction of this market and vendors’ ability to solve their operational and organizational requirements. Hybrid and multicloud environments. Firms today live and operate in a hybrid setup — a mix of on-premises and public cloud services. Applications, infrastructure, and data are spread across this setup, and workload/batch automation must likewise seamlessly integrate across it. Native capabilities in business applications. Some business applications have native capabilities to perform workload automation. We will explore how these impact standalone tools in the market. AI and AI agent enhancements. While AI is no secret in automation, we want to make clear how AI will help advance solutions. When should agents take over (if at all)? Demand for operational and cyber resiliency. With the growing threat of system failures and cybersecurity issues, all automation solutions must be designed with capabilities to address these challenges. Workload/batch automation can no longer be just a tool for the IT organization: Like all other types of automation, it must be a strategic enabler for modern businesses. By revisiting research in this space, we will explore new possibilities for scalability, efficiency, and resilience. Get Involved Over the next two months, we will be conducting interviews and taking briefings with vendors. If you would like to participate in our research, please contact Meg Bellavance ([email protected]). source

As a keen amateur chef, I have been known to occasionally seek inspiration from a tv cooking competition. Those bite-sized episodes of culinary drama sometimes provide just enough to satisfy my hunger for light evening entertainment. Of course, all these shows follow a proven recipe: enthusiastic contestants, challenging ingredients, and a panel of picky jurors deciding everyone’s fate – all set against the backdrop of the ever-present ticking clock… At the end of each episode, there is always a winner – a Chopped Champion, a Top Chef, a Star Baker. The victor is the one who, through the various phases of the competition, wins over the expectant jurors by their transformation of the raw ingredients, while simultaneously wrangling the technology, dealing with the heat of the kitchen, and letting the viewers know just enough about their unique and scintillating backstory. Are they chefs, or are they marketers? For every winner, there are of course multiple losers – the eliminated ones. These unfortunate contestants tend to falter for a few simple reasons.  While talented and accomplished competitors, they often fail to adapt their usual cooking approaches to the specific demands of the competition arena. They make an error either in what to serve, how to prepare it, or how it is presented. And no one likes medium-rare chicken, even on a bed of seasonal yuzu-drizzled kale chips… Recurring Revenue Marketing Demands A Different Recipe Seasoned B2B marketers often face similar challenges when stepping into the competitive recurring revenue arena. Equipped with their trusted tools, know-how and scars from years of competition, they often ‘play it too safe.’ They apply tried and tested [legacy] approaches to their new environment, only to be greeted with an underwhelmed reaction from a new jury of buyers. It is not that they have suddenly become bad marketers, however. Recurring revenue marketing in B2B is still marketing, with the raw ingredients of brand, demand, engagement, and enablement. It is just that these ingredients need to be prepared and seasoned in different ways, to reflect the nature and demands of the recurring revenue environment. Optimizing Five Stakeholder Relationships Will Help Your Recurring Revenue Rise In our recent research report “Recurring Revenue Marketing Demands Customer Obsession And A Seamless Operating Model“, Dawn Ferrara and I explain how the secret to recurring revenue marketing success is to reimagine marketing’s work through the lens of its interactions with 5 key stakeholder groups: Buyers, Product, Sellers, Operations, and Employees. We examine what makes these relationships different in a recurring revenue model, and introduce a new framework, the ‘Recurring Revenue Marketing Propeller.’ This framework illustrates how marketers should adjust their approach to stakeholder relationships, and what steps they should take to ensure they win the trust and long-term patronage of recurring revenue customers. We hope clients enjoy reading the full report. If we have left you hungry for more, please do not hesitate to contact us to schedule a deeper discussion. source

Embracing The Future Of Work With Knowledge Knowledge management is changing before our eyes. The past decade has seen little advancement in knowledge management (KM) solutions, practices, or standards, leaving organizations to repeatedly relaunch, hoping to gain better insights and decision-making. With the introduction of generative and conversational AI, knowledge management is returning. The Forrester Wave™ Knowledge Management Solutions, Q4 2024, highlights the critical role of knowledge management in driving business success and adaptability. Effective management of knowledge assets is essential for maintaining an edge in innovation and responsiveness. Specifically, knowledge management solutions play a pivotal role by enabling businesses to capture, store, and share internal know-how. With the right practice and supporting solutions, organizations can foster innovation and adaptability and ensure that their workforce remains agile and well informed. This approach underscores KM’s critical function in sustaining competitive advantage and cultivating a culture of ongoing improvement. Key Trends From The 2024 Knowledge Management Solutions Evaluation Generative AI Impact Has Begun In Earnest AI capabilities redefine KM solutions, offering more intelligent ways to categorize, search, and personalize user content. The leading solutions in 2024 have deeply integrated AI to automate knowledge discovery and distribution, making it easier for employees to find relevant information when needed. Customers are still leery of full adoption, and many are using these new capabilities in nonproduction environments until they are certain of the results. Enterprisewide Adoption Is Crucial KM solutions are increasingly designed to support cross-team collaboration and organizationwide knowledge sharing. Solutions that only serve a single persona may limit future adoption, highlighting the need for enterprisewide support. Experience Matters The best KM solutions in 2024 prioritize user experience with intuitive interfaces and seamless integration into daily workflows. As user adoption is critical to the success of any KM initiative, solutions that offer an engaging and frictionless experience stand out from the crowd. Intuitive interfaces, gamification, and seamless integration into existing workflows drive user engagement and adoption. Vendors are increasingly adopting a customer-centric approach, gathering feedback and insights to develop new features and enhancements that are relevant and practical for end users. Security And Compliance With the increasing volume of sensitive information being managed, security and compliance features are paramount. The leading solutions offer robust security measures, data encryption, and compliance with global regulations to ensure that knowledge assets are protected. Work Yet To Be Done With Knowledge Management Solutions While the vendors enhance and improve KM solutions’ capabilities, success largely depends on how organizations use these solutions. When speaking to referenced customers, these critical success factors stand out as key to field-level success. Cultural Change Needs To Be A Top Priority Adopting KM solutions, especially those incorporating AI, necessitates significant cultural changes within organizations. Traditional work methods are transformed by automating repetitive tasks and generating new knowledge solutions, which requires organizations to prepare their culture for these changes. Enhanced Metrics And Analytics Are Needed There is a growing emphasis on developing and utilizing advanced metrics and analytics to measure the success and value of KM solutions. Organizations are encouraged to baseline metrics before adoption and create meaningful dashboards to track progress and impact. Additionally, organizations should focus on evolving KM solution metrics and analytics to measure success and value creation effectively. The Forrester Wave evaluation on knowledge management solutions for Q4 2024 paints a promising picture for the future of knowledge work. By leveraging KM solutions, organizations can enhance their knowledge management practices, empower their workforce, and maintain a competitive edge in the information age. Click here for the full report: The Forrester Wave™: Knowledge Management Solutions, Q4 2024. Let’s Connect Have questions? That’s fantastic. Let’s connect and continue the conversation! Please reach out to me through social media or request a guidance session. Follow my blogs and research at Forrester.com. source

We need to talk about Texas Attorney General Ken Paxton. The Texas Data Privacy and Security Act partially went into effect July 1 and went into full effect on January 1, 2025. Ahead of the law, the attorney general announced a “data privacy and security initiative,” essentially teasing all the enforcement actions his office would take. Since then, his office has filed numerous privacy-related lawsuits and investigations, going after companies that: Collect, use, and sell data without consent. The Texas AG sued Allstate and its subsidiary Arity for capturing consumers’ location data and using it to feed a “driving behavior database,” which other insurance companies could use to adjust rates and premiums. In March, The New York Times reported that car companies collect driving data, often without consent, and resell it to insurance companies. Texas was the first state to take legal action, suing GM in August. Share sensitive data inappropriately. SiriusXM, MyRadar, Miles, and Tapestri were all caught in the AG’s crosshairs for sharing sensitive data without proper consent and/or disclosure. All four companies collected users’ precise location data; SiriusXM allegedly collected vehicle data, as well. The state court is also hearing a case against Google that claims the company collected Texans’ biometrics data without consent; Meta already settled a similar suit for $1.4 billion. (These biometrics lawsuits were filed in 2022, before the state law.) Fail to adequately protect children’s data. Similar to federal regulators, Texas has also been very busy enforcing children’s privacy protections. The AG sued TikTok and launched investigations into Character.AI, Instagram, Reddit, Discord, and others, all centered on whether they protected minors’ data to the extent required by the law and met parental control and consent requirements. The State Regulatory Patchwork Is Painful, But You Can’t Ignore It The Texas law is one of 13 state privacy laws already in effect, with six more going into effect over the next year. The attorney general’s running list of lawsuits and investigations provides important caveats for companies: Don’t put all your eggs in the federal administration’s basket. Many companies and consumers are expecting a significantly different regulatory landscape with the Trump administration. While the future of the Consumer Financial Protection Bureau and other key agencies is unknown, Texas reminds us that states can still wield their own power, separate from the federal tides. Keep an eye on state enforcement. California has gotten the most attention for having the most stringent state privacy law, and it has the benefit of being a first mover. But as Texas’ flurry of activity shows, other state laws could be a bigger focal point. Even if their requirements are weaker, more active or more stringent enforcement could call for more compliance resources. Stay on the right side of consumers’ expectations. In some cases, the first mover to take legal action for a privacy breach isn’t regulators but consumers themselves. They’ve stepped in to sue Google for its misleading Incognito mode disclosures, LinkedIn for using their data to train AI models without proper consent, and Patagonia and The Home Depot for disclosing consumer data to third parties. Consumers are increasingly aware of the extent to which their data is shared, and they are paying more attention to privacy policies and disclosures. When making decisions about data collection and disclosure, factor in not just what’s legally allowed but also what consumers will realistically be comfortable with. If you’re a Forrester client and need guidance on consumer privacy attitudes and data strategy decisions, set up a guidance session, and be sure to check out The Forrester Take for ongoing privacy developments in the B2C marketing space. Happy Data Privacy Day! source

We are thrilled to announce our research, Deconstructing Human-Element Breaches (Forrester clients can access here), detailing the many and varied risks posed by and to humans — problems that have plagued cybersecurity teams for decades. Forrester clients can use this research as a catalyst for productive conversations with executives and peers across functions about controls to mitigate the human-element breach types most common to their organizations and industries. This blog includes an FAQ based on the most common questions we receive from our clients and the security vendor community about human-element or human-related breaches. Aren’t human-element breaches just social engineering and human error? Whenever we mention human-related breaches, security and risk leaders and practitioners typically think of two main categories: social engineering and human error. This isn’t wrong but isn’t the full picture. After covering these topics separately for years, we decided to deconstruct the problem of human element breaches to uncover what they are and how to address them. This includes a variety of categories such as security culture, social engineering (including phishing), and insider risk. How do I use Forrester’s wheel of human-element breaches? As part of the research, we deconstructed eight breach families containing 25 human-element breach types (see figure below). They include established and emerging attacks such as social engineering, data exfiltration by insiders, and just plain human error. Attackers target humans in so many different ways, and humans behave in such distinct ways that leave them and their organizations vulnerable to attacks. Security leaders can use this wheel to assess the breach types that pose the most risk to their organization, define and describe each breach to stakeholders, and gain buy-in for investment to mitigate these risks. Why do we need this clarity? While it’s great that human-centered security is becoming more top of mind, human-related breaches remain inconsistently defined. For example, well-respected sources, such as the annual Verizon Data Breach Investigations Report, the European Union Agency for Cybersecurity, and the Office of the Australian Information Commissioner’s notifiable data breach reports, each provide different perspectives of what constitutes human-related breaches. This confusion can lead organizations to focus on common breaches while ignoring others, limit the solutions to well-trodden yet ineffective recommendations such as security awareness and training (SA&T), or worse, bury their heads in the sand, overfocusing on technology and not people. Can’t you just train people? After all, this is “just” a human issue. According to Forrester data, 97% of organizations conduct some form of SA&T — hoping for a silver bullet while checking a regulatory compliance box. Despite this, human-related attacks such as business email compromise have quadrupled, CISOs haven’t instilled security cultures in their organizations, training continues to cause friction for learners, and no one knows what behaviors actually change. While awareness of security issues is important, it can never replace the role of technical controls. Even the most vigilant employee will fall for a credible phishing lure or deepfake voice call, accidentally misconfigure an API setting, or send a sensitive file to the wrong recipient. Training is not enough. Technical controls must be in place to protect users from these attacks and change their behavior. If training isn’t as effective as you say it is, can’t we just use tech? While some breaches, such as those caused by human error or social engineering, are easy to associate with people, others that are technologically heavy, such as generative AI (genAI) misuse, are a bit more difficult to understand. Yet it was people relying on fallible genAI content that led the Australian Federal Parliament to publish an inaccurate submission. Without understanding that this is a human-related issue, it is easy to try to rely solely on technology to solve the problem. Security leaders need to strike a balance between training and technical controls. We provide guidance on how to do so using Forrester’s Human-Element Breach Control Matrix. I keep hearing about human risk management, but isn’t it just SA&T 2.0? Far from being SA&T with a fancy new name, human risk management (HRM) solutions present a significant change of mindset, strategy, process, and technology. Forrester defined HRM and began evaluating HRM vendors, encouraging orgs to positively influence security behaviors through evidence-based detection and anticipation of human risk, instead of purely relying on training. Do we really need another tool to manage the human risk? While some technologies in your tech stack provide limited behavioral insights, HRM is unique in that its sole focus is human risk. It integrates with existing tools and technology to measure a vast range of security behaviors and provides a comprehensive view of human risk. HRM also correlates behavioral, threat, access, and knowledge data to surface previously unseen risks. It interacts with people through a set of interventions including training but also through policy updates to protect people in a way that requires minimal effort on their part. Talk To Us Forrester clients can schedule a guidance session or inquiry with: Jinan Budge, for human-centered security, security culture, influence and engagement, and human risk management. Jess Burn, for social engineering and email, messaging, and collaboration security solutions. Joseph Blankenship, for insider risk. Heidi Shey, for data security. Any one of the contributors to this research to discuss the entirety of human-related breaches. source

Exciting developments such as DeepSeek’s R1 announcement are extending opportunities to run large language models (LLMs) on edge devices. These advancements could have profound implications for edge computing, particularly in the realms of AIOps (artificial intelligence for IT operations) and observability. By enabling real-time insights and faster automations at the edge, enterprises can enhance their operational posture, drive down costs, and improve operational efficiency and resilience. The Impact On Edge Computing Edge computing has been gaining traction to process data closer to its source, reducing latency and bandwidth usage. Edge computing technologies help firms anticipate customer needs, act on their behalf, and operate businesses efficiently in localized contexts including internet-of-things-enabled scenarios. Running LLMs on laptops and edge devices enhances these benefits by delivering powerful AI capabilities right at the edge. Training these models is a considerable challenge, something synthetic data could play a role in for AIOps, which is an approach that DeepSeek appears to have leveraged. DeepSeek-R1 claims to be as good if not better than other top-tier models, but it also offers unique advantages such as the ability to explain its answers by default. This transparency is crucial for building trust and understanding in AI-driven decisions in AIOps solutions. Processing and analyzing vast amounts of data in real time at the edge enables more responsive and intelligent edge devices. This capability is particularly valuable in scenarios when immediate decision-making is critical but connectivity to a central source or cloud resources is intermittent and unreliable. Alternative considerations are the high costs for networking and risks associated with data traveling from the edge to the cloud and data center. Some AIOps strategic objectives are to improve prediction accuracy, enhance user experiences, and produce far-reaching contextual insights for IT operations; all these stand to benefit from LLMs processing telemetry at the edge. Enhancing AIOps And Observability AIOps and observability are crucial components of modern IT operations, providing the tools needed to monitor, analyze, and optimize complex systems. Observability tools capture real-time data points, including metrics, events, logs, and traces (MELT), which are essential for understanding system behavior and performance. AIOps leverages this data to reduce alert noise, troubleshoot issues, automate remediation, and provide deep, contextual real-time insights. With LLMs running on edge devices, AIOps and observability can achieve new levels of real-time insight and automation. For instance, LLMs can analyze MELT data on the fly, identifying patterns and anomalies that might indicate potential issues, security or operational. The immediate analysis allows for quicker detection and resolution of problems, minimizing downtime and enhancing system reliability especially in environments with unreliable or irregular connectivity. The integration of smaller-footprint LLMs that can run at the edge, such as DeepSeek-R1, with AIOps can also lead to more proactive and predictive maintenance of devices and infrastructure or injection of risk-mitigating actions with no human intervention. A New Paradigm For IT Operations The integration of LLMs with edge computing and AIOps and observability represents a new paradigm for IT operations. It could be a game-changer for edge computing, AIOps, and observability if the advances of DeepSeek and others that are sure to surface run their course. This approach enables enterprises to harness the full potential of AI at the edge, driving faster and more informed decision-making. It also allows for a more agile and resilient IT infrastructure, capable of adapting to changing conditions and demands. As enterprises embrace this new paradigm, they must rethink their data center and cloud strategies. The focus will shift to a hybrid and distributed model, dynamically allocating AI workloads between edge devices, data centers, and cloud environments. This flexibility will optimize resources, reduce costs, and enhance IT capabilities, transforming data center and cloud strategies into a more distributed and agile landscape. At the center will remain observability and AIOps platforms, with the mandate for data-driven automation, autoremediation, and broad contextual insights that span the entire IT estate. Join The Conversation Register for the upcoming webinar on February 12, The Importance Of AI-Driven IT Operations And AIOps In Edge, IoT, And OT Computing. During this webinar, I will be speaking with my colleague Michele Pelino about these very topics that DeepSeek has further catapulted into the news. As always, I invite you to reach out through social media to any of us if you want to provide general feedback. If you prefer more formal or private discussions, email [email protected] to set up a meeting! You can also follow our research at Forrester.com by clicking on any of our names below. Click the names to follow our research at Forrester.com: Carlos Casanova, Michele Pelino, and Michele Goetz. source