Why security stacks need to think like an attacker, and score every user in real time

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Why security stacks need to think like an attacker, and score every user in real time

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More More than 40% of corporate fraud is now AI-driven, designed to mimic real users, bypass traditional defenses and scale at speeds that overwhelm even the best-equipped SOCs. In 2024, nearly 90% of enterprises were targeted, and […]

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More than 40% of corporate fraud is now AI-driven, designed to mimic real users, bypass traditional defenses and scale at speeds that overwhelm even the best-equipped SOCs.

In 2024, nearly 90% of enterprises were targeted, and half of them lost $10 million or more.

Bots emulate human behavior and create entire emulation frameworks, synthetic identities, and behavioral spoofing to pull off account takeovers at scale while slipping past legacy firewalls, EDR tools, and siloed fraud detection systems.

Attackers weaponize AI to create bots that evade, mimic, and scale

Attackers aren’t wasting any time capitalizing on using AI to weaponize bots in new ways. Last year, malicious bots comprised 24% of all internet traffic, with 49% classified as ‘advanced bots’ designed to mimic human behavior and execute complex interactions, including account takeovers (ATO).

Over 60% of account takeover (ATO) attempts in 2024 were initiated by bots, capable of breaching a victim’s credentials in real time using emulation frameworks that mimic human behavior. Attacker’s tradecraft now reflects the ability to combine weaponized AI and behavioral attack techniques into a single bot strategy.

That’s proving to be a lethal combination for many enterprises already battling malicious bots whose intrusion attempts often aren’t captured by existing apps and tools in security operations centers (SOCs).

Malicious bot attacks force SOC teams into firefighting mode with little or no warning, depending on the legacy of their security tech stack.

“Once amassed by a threat actor, they can be weaponized,” Ken Dunham, director of the threat research unit at Qualys recently said. “Bots have incredible resources and capabilities to perform anonymous, distributed, asynchronous attacks against targets of choice, such as brute force credential attacks, distributed denial of service attacks, vulnerability scans, attempted exploitation and more.”

From fan frenzy to fraud surface: bots corner the market for Taylor Swift tickets

Bots are the virtual version of attackers who can scale to millions of attempts per second to attack a targeted enterprise and increasingly high-profile events, including concerts of well-known entertainers, such as Taylor Swift.

Datadome observes that the worldwide popularity of Taylor Swift’s concerts creates the ROI attackers are looking for to build ticket bots that automate what scalpers do at scale. Ticket bots, as Datadome calls them, scoop up massive quantities of tickets at the world’s most popular events and then resell them at significant markups.

The bots flooded Ticketmaster and were a large part of a surge of 3.5 billion requests that hit the ticket site, causing it to crash repeatedly. Thousands of fans were unable to access the presale group, and ultimately, the general ticket sale had to be canceled.

Swarms of weaponized bots froze tens of thousands of Swifties from attending her last Eras concert tour. VentureBeat has learned of comparable attacks on the world’s leading brands on their online stores and presence globally. Dealing with bot attacks at that scale, powered by weaponized AI, is beyond the scope of an e-commerce tech stack to handle – they’re not built to deal with that level of security threat.

“It’s not just about blocking bots—it’s about restoring fairness,” Benjamin Fabre, CEO of DataDome, told VentureBeat in a recent interview. The company helped Se e Tickets deflect similar scalping attacks in milliseconds, distinguishing fans from fraud using multi-modal AI and real-time session analysis.

Bot attacks weaponized with AI often start by targeting login and session flows, bypassing endpoints in an attempt not to be detected by standard web application firewalls (WAF) and endpoint detection and response (EDR) tools. Such sophisticated attacks must be tracked and contained in a business’s core security infrastructure, managed from its SOC.

Why SOC teams are now on the front line

Weaponized bots are now a key part of any attacker’s arsenal, capable of scaling beyond what fraud teams alone can contain during an attack. Bots have proven lethal, taking down enterprises’ e-commerce operations or, in the case of Ticketmaster, a best-selling concert tour worth billions in revenue.

As a result, more enterprises are bolstering the tech stacks supporting their SOCs with online fraud detection (OFD) platforms. Gartner’s Dan Ayoub recently wrote in the firm’s research note Emerging Tech Impact Radar: Online Fraud Detection that “organizations are increasingly waking up to the understanding that ‘fraud is a security problem’ as is becoming evident in adoption of some of the emerging technologies being leveraged today”.

Gartner’s research and VentureBeat’s interviews with CISOs confirm that today’s malicious bot attacks are too fast, stealthy and capable of reconfiguring themselves on the fly for siloed fraud tools to handle. Weaponized bots have long been able to exploit gaps between WAFs, EDR tools and fraud scoring engines, while also evading static rules that are so prevalent in legacy fraud detection systems.

All these factors and more are why CISOs are bringing fraud telemetry into the SOC.

Journey-Time Orchestration is the next wave of online fraud detection (OFD)

AI-enabled bots are constantly learning how to bypass long-standing fraud detection platforms that rely on sporadic or single point-in-time checks. These checks include login validations, transaction scoring tracking over time, and a series of challenge-responses. While these were effective before the widespread weaponization of bots, botnets and networks, AI-literate adversaries now know how to exploit context switching and, as many deepfakes attacks have proven, know how to excel at behavioral mimicry.

Gartner’s research points to Journey Time Orchestration (JTO) as the defining architecture for the next wave of OFD platforms that will help SOCs better contain the onslaught of AI-driven bot attacks. Core to JTO is embedding fraud defenses throughout each digital session being monitored and scoring risk continuously from login to checkout to post-transaction behavior.

Journey-Time Orchestration continuously scores risk across the entire user session—from login to post-transaction—to detect AI-driven bots. It replaces single-point fraud checks with real-time, session-wide monitoring to counter behavioral mimicry and context-switching attacks. Source: Gartner, Innovation Insight: IAM Journey-Time Orchestration, Feb. 2025

Who’s establishing an early lead in Journey Time Orchestration defense

DataDome, Ivanti and Telesign are three companies whose approaches show the power of shifting security from static checkpoints to continuous, real-time assessments is paying off. Each also shows why the future of SOCs must be predicated on real-time data to succeed. All three of these companies’ platforms have progressed to delivering scoring for every user interaction down to the API call, delivering greater contextual insight across every behavior on every device, within each session.

What sets these three companies apart is how they’ve taken on the challenges of hardening fraud prevention, automating core security functions while continually improving user experiences. Each combines these strengths on real-time platforms that are also AI-driven and continually learn – two core requirements to keep up with weaponized AI arsenals that include botnets.

DataDome: Thinking Like an Attacker in Real Time

DataDome, A category leader in real-time bot defense, has extensive expertise in AI-intensive behavioral modeling and relies on a platform that includes over 85,000 machine learning models delivered simultaneously across 30+ global PoPs. Their global reach allows them to inspect more than 5 trillion data points daily. Every web, mobile and API request that their platform can identify is scored in real time (typically within 2 milliseconds) using multi-modal AI that correlates device fingerprinting, IP entropy, browser header consistency and behavior biometrics.

“Our philosophy is to think like an attacker,” Fabre told VentureBeat. “That means analyzing every request anew—without assuming trust—and continuously retraining our detection models to adapt to zero-day tactics”.

Unlike legacy systems, which lean on static heuristics or CAPTCHAs, DataDome’s approach minimizes friction for verified, legitimate users. Its false-positive rate is under 0.01%, meaning fewer than 1 in 10,000 human visitors see a challenge screen. Even when challenged, the platform invisibly continues behavior analysis to verify the user’s legitimacy.

“Bots aren’t just solving CAPTCHAs now—they’re solving them faster than humans,” Fabre added. “That’s why we moved away from static challenges entirely. AI is the only way to beat AI-driven fraud at scale”.

Case in point: See Tickets used DataDome to defend against the same bot-driven scalping wave that crashed Ticketmaster during the Taylor Swift Eras Tour. DataDome could distinguish bots from fans in milliseconds and prevent bulk buyouts, preserving ticket equity during peak load. In luxury retail, brands like Hermès deploy DataDome to protect high-demand drops (e.g., Birkin bags) from automated hoarding.

Ivanti Extends Zero Trust and exposure management into the SOC

Ivanti is redefining exposure management by integrating real-time fraud signals directly into SOC workflows through its Ivanti Neurons for Zero Trust Access and Ivanti Neurons for Patch Management platforms. “Zero trust doesn’t stop at logins,” Mike Riemer, Ivanti Field CISO told VentureBeat during a recent interview. “We’ve extended it to session behaviors including credential resets, payment submissions, and profile edits are all potential exploit paths.”

Ivanti Neurons continuously evaluates device posture and identity behavior, flagging anomalous activity and enforcing least-privilege access mid-session. “2025 will mark a turning point,” added Daren Goeson, SVP of product management at Ivanti. “Now defenders can use GenAI to correlate behavior across sessions and predict threats faster than any human team ever could.”

As attack surfaces expand, Ivanti’s platform helps SOC teams detect SIM swaps, mitigate lateral movement and automate dynamic microsegmentation. “What we currently call ‘patch management’ should more aptly be named exposure management or how long is your organization willing to be exposed to a specific vulnerability?” Chris Goettl, VP of product management for endpoint security at Ivanti told VentureBeat. “Risk-based algorithms help teams identify high-risk threats amid the noise of numerous updates.”

“Organizations should transition from reactive vulnerability management to a proactive exposure management approach,” added Goeson. “By adopting a continuous approach, they can effectively protect their digital infrastructure from modern cyber risks.”

Telesign’s AI-driven identity intelligence pushes fraud detection to session scale

Telesign is redefining digital trust by bringing identity intelligence at session scale to the front lines of fraud detection. By analyzing more than 2,200 digital identity signals ranging from phone number metadata to device hygiene and IP reputation, Telesign’s APIs deliver real-time risk scores that catch bots and synthetic identities before damage is done.

“AI is the best defense against AI-enabled fraud attacks,” said Telesign CEO Christophe Van de Weyer in a recent interview with VentureBeat. “At Telesign, we are committed to leveraging AI and ML technologies to combat digital fraud, ensuring a more secure and trustworthy digital environment for all.”

Rather than relying on static checkpoints at login or checkout, Telesign’s dynamic risk scoring continuously evaluates behavior throughout the session. “Machine learning has the power to constantly learn how fraudsters behave,” Van de Weyer told VentureBeat. “It can study typical user behaviors to create baselines and build risk models.”

Telesign’s Verify API underscores its omnichannel strategy, enabling identity verification across SMS, email, WhatsApp, and more, all through a single API. “Verifying customers is so important because many kinds of fraud can often be stopped at the ‘front door,’” Van de Weyer noted in a recent VentureBeat interview.

As generative AI accelerates attacker sophistication, Van de Weyer issued a clear call to action: “The emergence of AI has brought the importance of trust in the digital world to the forefront. Businesses that prioritize trust will emerge as leaders in the digital economy.” With AI as its backbone, Telesign looks to turn trust into a competitive advantage.

Why fraud prevention’s future belongs in the SOC

For fraud protection to scale, it must be integrated into the broader security infrastructure stack and owned by the SOC teams who use it to avert potential attacks. Online fraud detection platforms and apps are proving just as critical as APIs, Identity and Access Management (IAM), EDRs, SIEMs and XDRs. VentureBeat is seeing more security teams in SOCs take greater ownership of validating how consumer transactions are modeled, scored and challenged.

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Another major shortage – which should not be news to anyone – is power. Lynch said that it is the primary reason many data centers are moving out of the heavily congested areas, like Northern Virginia and Santa Clara, and into secondary markets. Power is more available in smaller markets than larger ones. “If our client needs multi-megawatt capacity in Silicon Valley, we’re being told by the utility providers that that capacity will not be available for up to 10 years from now,” so out of necessity, many have moved to secondary markets, such as Hillsborough, Oregon, Reno, Nevada, and Columbus, Ohio. The growth of hyperscalers as well as AI is driving up the power requirements of facilities further into the multi-megawatt range. The power industry moves at a very different pace than the IT world, much slower and more deliberate. Lynch said the lead time for equipment makes it difficult to predict when some large scale, ambitious data centers can be completed. A multi-megawatt facility may even require new transmission lines to be built out as well. This translates into longer build times for new data centers. CBRE found that the average data center now takes about three years to complete, up from 2 years just a short time ago. Intel, AMD, and Nvidia haven’t even laid out a road map for three years, but with new architectures coming every year, a data center risks being obsolete by the time it’s completed. However, what’s the alternative? To wait? Customers will never catch up at that rate, Lynch said. That is simply not a viable option, so development and construction must go on even with short supplies of everything from concrete and steel to servers and power transformers.

Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs). In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle