Are you one of the 180 million Americans estimated to have an Amazon Prime subscription? Or one of the 57 million who paid for an online news service last year? These familiar examples underscore the reality of customer experience (CX) in many industries today. Now increasingly delivered through digital channels, CX has been redefined by technology – making it a key part of the modern CIO’s mandate. The challenge for IT leaders is clear. They must ensure their digital infrastructure is set up to deliver the seamless, intelligent and hyper-personalized experience that customers expect. Otherwise, the impact on the business could be severe. CX’s commercial impact According to research by PwC, good CX has a tangible commercial impact and profoundly shapes people’s willingness to spend, stay loyal or switch to a competitor. Consumers are willing to pay a premium of up to 16% more for products or services with better CX. One-third of them will abandon a brand completely after a single negative interaction. With the growing significance of digital channels and the smart use of data for delivering great CX in today’s world, CIOs have understandably put this high up their priority list. “Customer experience is the cornerstone of digital transformation,” says Vipin Kalra, an expert in contact center technology with more than 20 years’ experience in the industry. “Successful digital transformation isn’t just about adopting new technology – it’s about elevating customer satisfaction, reducing friction, and making interactions more intuitive.” For Adobe’s Marie Knight, technology partnerships director, the CX challenge is evolving all the time. “We used to say that companies had to deliver the right message at the right time to their customers. But now there are so many digital channels, we talk about delivering the right message, at the right time, in the right place. And the place bit is becoming tricky.” Transformative shift A combination of the explosion in channels and technological tools is “blurring the lines between the CIO and CMO” in customer experience delivery, says Adobe’s Knight. “In the best organizations now, CIOs and CMOs work well together. AI is accelerating and complicating this, because content has to be secure and compliant as well as on-brand.” In Tata Communications’ work with customers in the US and around the world, “we’ve witnessed this transformative shift in CX up close,” says Raj Purkayastha, the company’s VP Head of Pre Sales and Strategy, Americas. “Many enterprises have worked hard to break down departmental silos, establish omnichannel offerings, and integrate multiple CX tools and partners into their digital environments,” Purkayastha continues. “That’s enabled them to create customer interactions that are increasingly contextual and trusted at scale.” But for too many organizations, this kind of positive outcome remains a distant dream – putting their commercial future at risk. The CX delivery gap In a study by Harvard Business Review, sponsored by Tata Communications, 94% of business leaders said that consistently delivering positive customer interactions is very important to business success in their industry. But a mere 38% said their organization is very successful at delivering them. It’s a frighteningly large gap between aspirations and reality. The causes of this giant delivery gap are both cultural and technological. Blamed the most are a lack of team collaboration (cited by 48% of business leaders) and a lack of the right talent (40%). But the challenge of siloed or disorganized customer data (39%) was also important, as was a lack of data analysis capability (39%) and a disparate set of tech tools used across the organization (35%). The CIO’s opportunity The Harvard Business Review findings serve to underscore the opportunity for CIOs. Modern software tools, especially AI-powered ones, should boost enterprises’ analytical capabilities substantially. Likewise, bringing multiple tools into a unified real-time platform can drastically enhance the value an organization gets from its piles of data. “These are precisely the kind of outcomes that Tata Communications’ Interaction Fabric was built to deliver,” highlights Purkayastha. “It helps CIOs with the essential changes they need to make so their digital infrastructure can meet customers’ modern expectations. Organizations can then enable interactions that are omni-channel, contextual and intelligent, providing a seamless experience for customers.” In all of these areas, CIOs can make the case for digital investments and infrastructure optimizations that tangibly benefit the bottom line. CX is more than just another IT initiative – it’s increasingly the competitive edge that defines market leaders. Driving that agenda forward is an exciting opportunity for CIOs to play a more central role in business success today. To learn more, visit Tata Communications’ website. source

Image: borodai/Envato Elements The U.K.’s Competition and Markets Authority has found that Apple is restricting competition in the country by limiting the use of rival browsers on its iOS devices and effectively requiring developers to use its WebKit browser engine. This, and a number of other concerns, is “holding back innovation and could be limiting growth in the UK.” Apple and Google own the two most popular mobile operating systems in the world, iOS and Android, meaning they are easily able to push their own browsers, Safari and Chrome, onto their users. If Google and Apple don’t take active steps to allow users to discover third-party browsers, they will never have any real competition, and therefore won’t be incentivised to innovate and provide the best possible products. They can also prevent third-party browsers from offering comparable or better service. Mobility must-reads How Apple’s restrictions impact competition Currently, all iOS browsers must use WebKit, which the CMA’s independent inquiry group says limits differentiation and inherently prevents them from matching Safari’s functionality and access. The group also found that Apple limits the amount of traffic and customisation options available for in-app browsing in third-party apps. Additionally, Apple receives significant payments from Google to maintain Google Search as the default search engine on Safari, a deal that reduces both companies incentives to compete in the browser market. On March 12, the CMA published its final report on the investigation into mobile browsers and cloud gaming markets in the U.K.. It confirmed all the concerns it highlighted back in November when it published its provisional findings, stating that its primary concerns remain focused on browser restrictions, while noting fewer issues in the cloud gaming market. SEE: Regulator CMA to Scrutinize Microsoft and Other Cloud Service Providers in the UK Regulatory efforts to promote fair competition Since the provisional report, Apple has released iOS 18.2 which makes it easier to switch the default browser from Safari, and Google showed how it limits the prompts that encourage users to set Chrome as default. Nevertheless, it still has some concerns relating to Safari and Chrome being pre-installed on Apple and Android devices, restricting user choice. The investigation was opened in 2021 when the CMA ruled that Apple and Google have an “effective duopoly on mobile ecosystems, including operating systems, app stores, and web browsers on mobile devices.” After the provisional report was published, Apple was concerned that making the recommended changes would “undermine user privacy and security.” Nevertheless, Apple has announced plans to allow browser engines other than WebKit on iOS and iPadOS in the EU due to the Digital Markets Act, though full implementation remains to be seen. In January, Google and Apple were announced as the first companies investigated for potential Strategic Market Status under the new U.K. Digital Markets, Competition and Consumers Act. If Google or Apple receives the designation, bespoke conduct requirements could be drafted for the company to follow, preventing anti-competitive practices in areas such as mobile ecosystems. The report recommends that any interventions should specifically ensure third-party browsers can innovate freely and allow users to choose their default browser. source

By Christopher Cole ( March 14, 2025, 6:44 PM EDT) — Opponents of the Federal Communications Commission’s nearly 30-year-old telecom subsidy system are making “strawman” arguments by claiming taxing power has been unlawfully delegated away from Congress, the government told the U.S. Supreme Court…. 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

Every technological revolution follows a pattern: an installation phase typified by eruption and frenzy, followed by a more stabilized period of deployment seeing steady growth to maturity. It’s a concept studied and introduced by researcher and consultant Carlota Perez as early as 2002.  At the start of these technological revolutions, the focus is on building the infrastructure. It’s the phase where early adopters reap huge rewards by developing the tools and platforms for future innovation. In the AI era, dominant players like OpenAI, Anthropic and Google laid the groundwork by creating powerful large language models (LLMs) and multimodal systems.  But AI’s initial Big Bang is almost over. It’s now entering a new phase. As the cost of AI infrastructure falls and access to these tools becomes more widespread, the competitive advantage will shift from owning infrastructure to applying new tech in novel ways.  This is not just theoretical. It’s a real-life pattern I lived as co-founder of Vungle, a mobile advertising platform that emerged within the mobile app economy. In 2011, when we started Vungle, mobile app development was still in its nascent stage. It was anyone’s game. We saw that current advertising models hadn’t yet adapted to mobile-native experiences. So, we addressed the pain point through high-quality video ads designed specifically for mobile games and apps. By the time mobile advertising became ubiquitous, Vungle was already well-positioned, which ultimately led to our $780 million acquisition.  Related:How to Turn Developer Team Friction Into a Positive Force If — as philosophy and Perez’ model suggest — history repeats itself, the next events in AI will play out as in the pattern of the 2010 mobile app revolution. When Apple launched the App Store, early adopters like Instagram, Uber and WhatsApp saw an opportunity to rethink entire business models around mobile-first user behavior. They were among the first to recognize how smartphones could change user interaction, distribution and monetization. They were also the biggest winners of the mobile app boom.  What’s to Expected Given AI’s growth  Just as having a mobile app is now table stakes for most businesses, AI-powered features will soon be expected rather than optional. Integrating AI for efficiency will be commonplace, not a differentiator. The real winners will be those applying AI in ways that make entirely new experiences possible. This is why the application layer of AI will create the most long-term value.  And just as most mobile companies that saw massive success weren’t infrastructure providers but companies that leveraged mobile effectively, the most successful AI companies won’t be building new AI models. They’ll be using AI to solve critical problems in industries like healthcare, finance and enterprise SaaS.   Related:3 Tech Deep Dives That CIOs Must Absolutely Make What IT Leaders Need to Do (and Fast!)  So, how can organizations stay ahead in this next phase of the AI revolution?  Start approaching AI as an enabler. AI’s time as a mere feature or tool to automate existing processes is done. You should go from “How can AI automate our tasks?” to “How can AI drive new business models?” A report from McKinsey said that corporate AI use-cases could yield long-term added productivity gains as high as $4.4 trillion. The same report shares three questions for leaders navigating this AI-centered future:  Is your AI strategy ambitious enough?   What does a successful AI adoption look like for your organization?   What skills define an AI-native workforce?  Prioritize AI-native products. Businesses should adopt — or better yet, pioneer AI-native solutions that fundamentally redefine user experiences and decision-making. Take Boardy for example. As early as we are into AI’s deployment era, it’s already found a niche (professional networking) to disrupt (by using AI to facilitate smarter, more personalized introductions), automating what was once an entirely manual process.  Related:What VC Investments Look Like in 2025 Invest in talent with AI-first thinking. Now’s the time to launch AI upskilling initiatives, such as AI certification programs or company-led AI boot camps. Hiring efforts should seek out AI-native product leaders, engineers and executives who will design products that incorporate AI from inception, rather than retrofitting it into legacy systems.  We are at a defining moment in the AI revolution. Access to foundational models is becoming democratized, and the real opportunity is shifting to how AI is applied in the real world. During the mobile app boom, our company succeeded because we saw how mobile-first thinking separated winners from also-rans in the early days of the App Store. The same will be true for AI. Companies that iterate and operate with AI’s unique capabilities will emerge as the dominant players of the next decade.  There’s no longer any doubt whether AI will change industries — it already is. The real question now: Who will be the AI-native companies that define this new era?  source

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Not that long ago, humans wrote almost all application code. But that’s no longer the case: The use of AI tools to write code has expanded dramatically. Some experts, such as Anthropic CEO Dario Amodei, expect that AI will write 90% of all code within the next 6 months. Against that backdrop, what is the impact for enterprises? Code development practices have traditionally involved various levels of control, oversight and governance to help ensure quality, compliance and security. With AI-developed code, do organizations have the same assurances? Even more importantly, perhaps, organizations must know which models generated their AI code. Understanding where code comes from is not a new challenge for enterprises. That’s where source code analysis (SCA) tools fit in. Historically, SCA tools have not provide insight into AI, but that’s now changing. Multiple vendors, including Sonar, Endor Labs and Sonatype are now providing different types of insights that can help enterprises with AI-developed code. “Every customer we talk to now is interested in how they should be responsibly using AI code generators,” Sonar CEO Tariq Shaukat told VentureBeat. Financial firm suffers one outage a week due to AI-developed code AI tools are not infallible. Many organizations learned that lesson early on when content development tools provided inaccurate results known as hallucinations. The same basic lesson applies to AI-developed code. As organizations move from experimental mode into production mode, they have increasingly come to the realization that code is very buggy. Shaukat noted that AI-developed code can also lead to security and reliability issues. The impact is real and it’s also not trivial. “I had a CTO, for example, of a financial services company about six months ago tell me that they were experiencing an outage a week because of AI generated code,” said Shaukat. When he asked his customer if he was doing code reviews, the answer was yes. That said, the developers didn’t feel anywhere near as accountable for the code, and were not spending as much time and rigor on it, as they had previously.  The reasons code ends up being buggy, especially for large enterprises, can be variable. One particular common issue, though, is that enterprises often have large code bases that can have complex architectures that an AI tool might not know about. In Shaukat’s view, AI code generators don’t generally deal well with the complexity of larger and more sophisticated code bases. “Our largest customer analyzes over 2 billion lines of code,” said Shaukat. “You start dealing with those code bases, and they’re much more complex, they have a lot more tech debt and they have a lot of dependencies.” The challenges of AI developed code To Mitchell Johnson, chief product development officer at Sonatype, it is also very clear that AI-developed code is here to stay. Software developers must follow what he calls the engineering Hippocratic Oath. That is, to do no harm to the codebase. This means rigorously reviewing, understanding and validating every line of AI-generated code before committing it — just as developers would do with manually written or open-source code.  “AI is a powerful tool, but it does not replace human judgment when it comes to security, governance and quality,” Johnson told VentureBeat. The biggest risks of AI-generated code, according to Johnson, are: Security risks: AI is trained on massive open-source datasets, often including vulnerable or malicious code. If unchecked, it can introduce security flaws into the software supply chain. Blind trust: Developers, especially less experienced ones, may assume AI-generated code is correct and secure without proper validation, leading to unchecked vulnerabilities. Compliance and context gaps: AI lacks awareness of business logic, security policies and legal requirements, making compliance and performance trade-offs risky. Governance challenges: AI-generated code can sprawl without oversight. Organizations need automated guardrails to track, audit and secure AI-created code at scale. “Despite these risks, speed and security don’t have to be a trade-off, said Johnson. “With the right tools, automation and data-driven governance, organizations can harness AI safely — accelerating innovation while ensuring security and compliance.” Models matter: Identifying open source model risk for code development There are a variety of models organizations are using to generate code. Anthopic Claude 3.7, for example, is a particularly powerful option. Google Code Assist, OpenAI’s o3 and GPT-4o models are also viable choices. Then there’s open source. Vendors such as Meta and Qodo offer open-source models, and there is a seemingly endless array of options available on HuggingFace. Karl Mattson, Endor Labs CISO, warned that these models pose security challenges that many enterprises aren’t prepared for. “The systematic risk is the use of open source LLMs,” Mattson told VentureBeat. “Developers using open-source models are creating a whole new suite of problems. They’re introducing into their code base using sort of unvetted or unevaluated, unproven models.” Unlike commercial offerings from companies like Anthropic or OpenAI, which Mattson describes as having “substantially high quality security and governance programs,” open-source models from repositories like Hugging Face can vary dramatically in quality and security posture. Mattson emphasized that rather than trying to ban the use of open-source models for code generation, organizations should understand the potential risks and choose appropriately. Endor Labs can help organizations detect when open-source AI models, particularly from Hugging Face, are being used in code repositories. The company’s technology also evaluates these models across 10 attributes of risk including operational security, ownership, utilization and update frequency to establish a risk baseline. Specialized detection technologies emerge To deal with emerging challenges, SCA vendors have released a number of different capabilities. For instance, Sonar has developed an AI code assurance capability that can identify code patterns unique to machine generation. The system can detect when code was likely AI-generated, even without direct integration with the coding assistant. Sonar then applies specialized scrutiny to those sections, looking for hallucinated dependencies and architectural issues that wouldn’t appear in human-written code. Endor Labs and

Teen girls and boys in the U.S. face different pressures and have different experiences at school but want the same things out of life (Willie B. Thomas/Getty Images) How we did this Pew Research Center conducted this study to better understand teens’ views on their school experiences, friendships and future plans. The Center conducted an online survey of 1,391 U.S. teens ages 13 to 17 from Sept. 18 to Oct. 10, 2024, through Ipsos. Ipsos recruited the teens via their parents, who were part of its KnowledgePanel. The KnowledgePanel is a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey was weighted to be representative of U.S. teens ages 13 to 17 who live with their parents by age, gender, race and ethnicity, household income, and other categories. Questions in this report that focus on students’ experiences in their school were not asked of the 91 students who said they are homeschooled. Here are the questions used for this report, along with responses, and the survey methodology­­­. This research was reviewed and approved by an external institutional review board (IRB), Advarra, an independent committee of experts specializing in helping to protect the rights of research participants. Terminology References to White and Black teens include those who identify as only one race and are not Hispanic. Hispanic teens are of any race. The views and experiences of Asian teens are not analyzed separately in this report due to sample limitations. Asian teens’ responses – and responses of teens from other racial and ethnic groups – are incorporated into the general figures throughout the report but are not analyzed separately due to sample limitations. All references to party affiliation include those who lean toward that party. Republicans include those who identify as Republicans and those who say they lean toward the Republican Party. Democrats include those who identify as Democrats and those who say they lean toward the Democratic Party. American teens face a host of challenges these days – both inside and outside the classroom. A new Pew Research Center survey of U.S. teens ages 13 to 17 finds that, while there is some common ground, many of the problems and pressure points teens are dealing with differ significantly for boys and girls. In addition, many teens see imbalances in how boys and girls are experiencing school and how they’re performing academically. Anxiety and depression tops the list of problems teens say their peers at school are dealing with, of the issues we asked about. Three-in-ten teens say it’s extremely or very common among their fellow students. And on balance, teens say anxiety and depression is more common among girls at their school than among boys. At the same time, majorities of teen boys and girls alike say girls have it easier when it comes to having friends they can turn to for emotional support. Academics are the biggest source of pressure for teens today. Roughly seven-in-ten (68%) say they personally feel a great deal or fair amount of pressure to get good grades. Girls and boys are about equally likely to say this (71% vs. 65%). Girls are significantly more likely than boys to say they face at least a fair amount of pressure to: Look good (55% vs. 39%) Fit in socially (45% vs. 37%) Greater shares of boys than girls say they face pressure to: Be physically strong (43% vs. 23%) Be good at sports (36% vs. 27%) Teens’ plans for the future Looking ahead, boys and girls want many of the same things out of life. Majorities of teens say it’s extremely or very important to them that as adults they have a job or career they enjoy (86%), have close friends (69%) and have a lot of money (58%). But they may take different paths to get there. Teen girls are significantly more likely than teen boys to say they plan to attend a four-year college after they graduate from high school (60% vs. 46%). In turn, boys are more likely than girls to say they’ll go to a vocational school (11% vs. 7%), work full time (9% vs. 3%) or join the military (5% vs. 1%) after high school. This survey – conducted Sept. 18-Oct. 10, 2024, among 1,391 teens ages 13 to 17 – focused on school experiences, friendships and future plans. Other key findings 42% of teens say girls at their school get better grades than boys. Only 3% say boys get better grades than girls, and 55% say things are about equal. Additionally, about one-in-four teens (27%) say girls at their school get more leadership positions than boys, while 16% say boys get more of these positions; 56% say there’s no differences. 63% of teens say boys are more disruptive in class than girls. Only 4% say girls are more disruptive, and 32% say there’s no difference. Inversely, teens are more likely to say girls speak up more in class than to say the same about boys (34% vs. 18%). Roughly half (48%) say there’s no difference. Almost all teens (98%) say they have at least one close friend, with 34% saying they have five or more. Boys and girls are equally likely to say they have at least one close friend. We also asked whether they think one group has it easier when it comes to having friends they can turn to for emotional support: A 58% majority of teens say girls do. Very few say boys have an easier time (7%), and 35% say it’s about the same for both. Thinking ahead to their adult lives, teens place less importance on getting married or having children than they do on job satisfaction, friendship and financial success. Republican teens and those who lean to the Republican Party are more likely than Democrats and Democratic leaners to say marriage and parenthood are important to them. source

Social media platform X was hit by a series of outages yesterday in what its owner Elon Musk has called a “massive cyberattack” stemming from Ukraine. Analysts believe the disruption was caused by botnets — but finding the culprits will be a tall order.   The outages began at around 14:00 CET and lasted for most of the afternoon, trailing off at around 18:00, according to Down Detector. While the outages appeared to have flatlined overnight, there was an uptick in reports of downtime starting from 08:00AM today up until the time of writing. “We’re not sure exactly what happened,” Musk said during a Fox Business interview. “But there was a massive cyberattack to try to bring down the X system, with IP addresses originating in the Ukraine area.”  In a separate post on X, Musk said: “We get attacked every day, but this was done with a lot of resources. Either a large, coordinated group and/or a country is involved.” The multi-billionaire has not provided any evidence to back up his claims.  TNW Conference – The 2025 Agenda has just touched down Discover the insightful and dare we say controversial sessions that will take place June 19-20. Toby Lewis, head of threat analysis at UK-based cybersecurity firm Darktrace, said the X outage appears to be a “fairly standard DDoS attack”, which involves many devices flooding a server or network with traffic to overwhelm and shut it down. “These sorts of attacks are almost always delivered by botnets — globally distributed networks of computers that have been unknowingly recruited to take part in the attack, typically through some form of compromise or the use of malware,” said Lewis. With botnets, hackers can control devices in a country without actually being there. So even if the IP addresses did originate Ukraine, as Musk claimed, that doesn’t necessarily mean the attack has any link to the country or its government.  Jake Moore, global cybersecurity advisor at Slovakian firm ESET, highlighted the method’s ability to conceal the culprits’ identities.  “DDoS attacks are a clever way of targeting a website without having to hack into the mainframe and therefore the perpetrators can remain largely anonymous and difficult to point a finger at,” he said. X adds to mounting problems for Musk Musk bought Twitter in October 2022 in a deal worth $44bn. Within a year, he had changed the platform’s name to X, laid off around 80% of the company’s staff and made sweeping changes to its content moderation policies.  Since Musk took over, there have been a series of glitches on the platform. X’s last major outage was in August last year, when hundreds of thousands of listeners were unable to access his interview with the then former US president Donald Trump. Musk later blamed the incident on a “massive” DDoS attack.    Whether yesterday’s attack had political or ideological motivations will be virtually impossible to ascertain. However, it comes amid a swathe of backlash against Musk, whose role in the US government and open support of far-right politicians is causing controversy and anger.  Tesla stock fell to a five-year low on Monday amid protests and arson attacks against the EV brand, which many see as symbolic of Musk, the company’s CEO and founder. A report last week found that Tesla sales in Germany fell 70% in February, off the back of a 60% sales slump in January.  Analysts have linked Tesla’s fallout to Musk’s increasingly incendiary behaviour. In Europe, Musk has openly endorsed Germany’s far-right AfD, even hosting an interview on X where he heaped praise on the party’s leader Alice Weidel. At the Trump inauguration on January 20, Musk made a controversial hand gesture, which many likened to a Nazi salute.  “There is no doubt that ‘the Musk factor’ has influenced Tesla’s sales in the same way as his reputation impacted Twitter when he bought it and rebranded it as X,” Andrew Fellows, an automotive industry expert at Star, a tech consultancy firm, previously told TNW. Musk’s antics have put Starlink, a subsidiary of his firm SpaceX, in hot water too. Officials in Europe are considering replacing the satellite internet service with a homegrown alternative amid mounting concerns about putting the continent’s security in the hands of a single, private network whose owner has direct ties to the US government. source

FTC Chair Andrew Ferguson on CNBC. Source: YouTube The Federal Trade Commission (FTC) will move ahead with a wide-ranging antitrust probe into Microsoft’s AI operations. Announced in the final days of the Biden administration, the Trump administration’s new FTC chair Andrew Ferguson will lead the probe. The FTC sent Microsoft a civil investigative demand late last year asking it to provide data about its AI models, including how training data is obtained and how much it costs to train an AI. The civil investigative demand stretches all the way back to 2016 and covers nearly a decade’s worth of data. What’s hot at TechRepublic Why the FTC is investigating Microsoft The agency will also investigate Microsoft for canceling some of its own internal AI development after agreeing to invest in competitor OpenAI. Microsoft did not disclose its investment in OpenAI to regulators ahead of time, as it should have. The FTC will investigate whether the deal was structured as a partnership in order to circumvent a merger investigation, which could have led to the deal being blocked. The FTC also asked for additional details about Microsoft’s data centers and the tech giant’s difficulty obtaining enough computing power to meet customer demands. It also wants more information about how Microsoft licenses software bundles — competitors have complained that bundles like Microsoft 365 make it difficult to compete against the software giant. The FTC said it is seeking to determine whether or not Microsoft’s other businesses give it an advantage over other AI companies and said it hopes to get a better grasp on cloud computing costs through the data provided by Microsoft. These additional details and data will help determine whether or not to bring a case against Microsoft. These investigations can take years and often don’t result in charges. Microsoft will likely seek to narrow the scope of the information and data requested, which is a common move during these investigations. FTC to pursue other cases against big tech companies This move indicates that new FTC chair Ferguson intends to keep investigating tech giants — a commitment he affirmed in his first public remarks at the end of February when he called investigating the tech sector his top priority. Microsoft is not the only tech company currently under FTC investigation. Ferguson inherited several cases from the Biden administration, including lawsuits against Amazon Inc. and Meta Inc. (which owns Facebook and Instagram). source

Cybersecurity vendors have come out of the woodwork in the past few months to announce their “agentic AI” innovations. These include vendors such as Swimlane, ReliaQuest, Dropzone AI, Intezer, and others. Some are announcing legitimate agentic AI features, while others are renaming existing ML or generative AI features to catch the hype: The blob strikes again! This has become further complicated as the definition and understanding of agentic AI capabilities have been as in flux as the rest of the generative AI market. After significant research and careful consideration, Forrester released a report defining agentic AI: Agentic AI Is Rising And Will Reforge Businesses That Embrace It (client-only access). According to this research, agentic AI is: Systems of foundation models, rules, architectures, and tools which enable software to flexibly plan and adapt to resolve goals by taking action in their environment, with increasing levels of autonomy. Agentic AI Is A Subset Of AI Agents To be explicitly clear: An AI agent is not the same thing as agentic AI. AI agents have been around for literal decades. Back when I was getting my computer engineering degree, I had to build an AI agent for an artificial intelligence class. The agent wasn’t anything crazy (certainly not to the level of generative AI) … it was a knowledge-based agent meant to understand and navigate the Wumpus world. The concept of agentic AI and its implementation are far from new. The challenge is that the majority of AI agents that have been developed don’t operate without human intervention. AI agents such as Waymo, Tesla, Apple’s Siri, and Amazon’s Alexa all require some level of human input, with Waymo being the most advanced by far. In contrast to AI agents, agentic AI is one or more AI agents that operate without human intervention. They learn and adapt to feedback and inputs to achieve a certain mission. Judges and critics are components that allow the system to perform more effectively, where the judge evaluates the output to ensure accuracy and the critic evaluates the output for specific flaws, biases, or ethical risks. In that way, agentic AI is a subset of AI agents that operate autonomously. Agentic AI Enables More Complex Use Cases Than RAG Agentic AI requires a different architecture than we see with retrieval-augmented generation (RAG), which only orchestrates pulling relevant information, not orchestrating a series of complex steps. Unlike agentic AI, RAG does not do significant planning or reasoning across information and cannot take adaptable action within enterprise environments. Agentic AI may use RAG as part of its capabilities, however. Security Tools Are Using Agentic AI To Automate Triage Agentic AI is being used to automate alert triage and some aspects of investigation in security tools. It has been particularly useful in automating triage and, in some cases, closing alerts related to phishing, though other use cases are on the horizon. While this blog breaks down what agentic AI is, we aren’t able to touch in depth on securing generative AI here. For advice on how to prepare for and secure agentic AI adoption in the enterprise, check out the report, Top Recommendations For Your Security Program, 2025. If you have more questions about how agentic AI is used in security tools, or if you want to talk about a particular vendor, book an inquiry or guidance session with me. source