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AI vs. AI: Prophet Security raises $30M to replace human analysts with autonomous defenders

Prophet Security, a startup developing autonomous artificial intelligence systems for cybersecurity defense, announced Tuesday it has raised $30 million in Series A funding to accelerate what its founders describe as a fundamental shift from human-versus-human to “agent-versus-agent” warfare in cybersecurity.The Menlo Park-based company’s funding round, led by venture capital firm Accel with participation from Bain Capital Ventures, comes as organizations struggle with an overwhelming volume of security alerts while sophisticated attackers increasingly leverage AI to scale and automate their operations. Prophet’s approach represents a marked departure from the “copilot” AI tools that have dominated the market, instead deploying fully autonomous agents that can investigate and respond to threats without human intervention.“Every security operations team is faced with a dual mandate of reducing risk while driving operational efficiency,” said Kamal Shah, Prophet Security’s co-founder and CEO, in an exclusive interview with VentureBeat. “Our Agentic AI SOC Platform addresses both challenges by automating manual, repetitive tasks in security operations with speed, accuracy and explainability.”The funding announcement coincides with Prophet’s launch of what it calls the industry’s most comprehensive Agentic AI SOC Platform, expanding beyond its initial Prophet AI SOC Analyst to include Prophet AI Threat Hunter and Prophet AI Detection Advisor. The platform represents a significant evolution from traditional Security Operations Center (SOC) automation tools, which typically rely on rigid, pre-programmed playbooks.

Prophet Security, a startup developing autonomous artificial intelligence systems for cybersecurity defense, announced Tuesday it has raised $30 million in Series A funding to accelerate what its founders describe as a fundamental shift from human-versus-human to “agent-versus-agent” warfare in cybersecurity.

The Menlo Park-based company’s funding round, led by venture capital firm Accel with participation from Bain Capital Ventures, comes as organizations struggle with an overwhelming volume of security alerts while sophisticated attackers increasingly leverage AI to scale and automate their operations. Prophet’s approach represents a marked departure from the “copilot” AI tools that have dominated the market, instead deploying fully autonomous agents that can investigate and respond to threats without human intervention.

“Every security operations team is faced with a dual mandate of reducing risk while driving operational efficiency,” said Kamal Shah, Prophet Security’s co-founder and CEO, in an exclusive interview with VentureBeat. “Our Agentic AI SOC Platform addresses both challenges by automating manual, repetitive tasks in security operations with speed, accuracy and explainability.”

The funding announcement coincides with Prophet’s launch of what it calls the industry’s most comprehensive Agentic AI SOC Platform, expanding beyond its initial Prophet AI SOC Analyst to include Prophet AI Threat Hunter and Prophet AI Detection Advisor. The platform represents a significant evolution from traditional Security Operations Center (SOC) automation tools, which typically rely on rigid, pre-programmed playbooks.


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Security teams drowning in 960 daily alerts face unprecedented capacity crisis

The cybersecurity industry faces a crisis of capacity and capability. Shah, who previously served as CEO of container security company StackRox before its acquisition by Red Hat, experienced these challenges firsthand. According to his observations, organizations receive an average of 960 security alerts daily, with up to 40% going uninvestigated due to resource constraints.

“The number one complaint that I see from customers every single day is too many alerts, too many false positives,” Shah explained. “If you think about the world that we live in today, on average, a company gets 960 alerts a day from all the security tools that they have in their environment, and 40% of those alerts are ignored because they just don’t have the capacity to go and investigate all those alerts.”

The problem is compounded by a severe shortage of skilled cybersecurity professionals. Shah points to what he calls a critical talent gap, noting there are 5 million open positions in cybersecurity globally, creating a situation where even organizations with budget to hire cannot find qualified personnel.

Prophet’s solution directly addresses this capacity crunch. Over the past six months, the company’s AI SOC Analyst has performed more than 1 million autonomous investigations across its customer base, saving an estimated 360,000 hours of investigation time while delivering 10 times faster response times and reducing false positives by 96%.

How autonomous AI agents differ from reactive copilot systems transforming cybersecurity

The distinction between Prophet’s “agentic” AI and the copilot models deployed by larger cybersecurity vendors like CrowdStrike, Microsoft, and Sentinel One is fundamental to understanding the company’s value proposition. Traditional copilot systems require human analysts to initiate queries and interpret responses, essentially serving as sophisticated search interfaces for security data.

“Copilot is reactive,” Shah explained. “You have an alert come in and a security analyst has to go and write questions, ask the question to say, hey, what does this mean? And you have to know what questions to ask. The analyst is still in the loop for every single alert that comes in because they’re interacting with it.”

By contrast, Prophet’s agentic AI proactively initiates investigations the moment an alert is triggered, autonomously gathering evidence, reasoning through the data, and reaching conclusions without human intervention. The system documents every step of its investigation process, creating an audit trail that allows security teams to understand and verify its reasoning.

“What Prophet AI is able to do is immediately, once an alert is triggered, it proactively goes and completes the investigation,” Shah said. “Within a matter of minutes, your investigation is complete and it knows what questions to ask, and it’s been trained to act like an expert analyst.”

Building enterprise trust through transparent AI decision-making and data protection

Prophet’s system leverages multiple frontier AI models, including offerings from OpenAI, Anthropic, and others, selecting the most appropriate model for each specific task. The company has built what Shah describes as an “evals framework” to ensure accuracy, repeatability, and consistency while preventing AI hallucinations—a critical concern in security contexts where false information can lead to inappropriate responses.

“In security, you are in a trust building exercise with the security teams, and if you hallucinate, you’re going to lose trust and they’re not going to use your product,” Shah emphasized. The company employs a retrieval-augmented generation (RAG) architecture combined with rigorous evaluation processes to maintain what Shah calls “a high bar for security teams.”

Data privacy and security represent paramount concerns for Prophet’s enterprise customers. The company employs a single-tenant architecture ensuring customer data remains isolated, and maintains contractual agreements with AI model providers preventing customer data from being used to train or fine-tune models.

Early customers report dramatic efficiency gains as AI handles thousands of security alerts

Prophet’s customer base includes Docker, which provided a testimonial for the funding announcement. Tushar Jain, Docker’s EVP of Engineering and Product, noted that “Prophet AI is already helping streamline parts of our security workflow, and we’re just getting started. With the recent release of Threat Hunter and growing integration with our systems, we see a clear path to faster response times, reduced noise, and a more focused security team.”

The company has also published case studies demonstrating dramatic improvements in SOC efficiency. Eric Wille, CISO at Cabinet Works, reported reducing his team’s alert volume from 33,200 down to just six alerts requiring human attention, effectively allowing his small team to operate with the efficiency of a much larger organization.

“Prophet AI cut our alert queue from thousands to dozens,” Wille said in a video testimonial. “It’s a force multiplier that removes investigation bottlenecks, improves analyst focus, and helps us respond to real threats faster.”

Rising cyber threats and evolving attack methods drive demand for AI-powered defense

Prophet’s emergence occurs against a backdrop of rapidly evolving cyber threats. CrowdStrike’s 2025 Global Threat Report documented a 150% increase in China-nexus cyber activity and a 442% growth in voice phishing operations, while noting that 79% of detected threats were malware-free, making them harder to identify through traditional signature-based detection methods.

The company’s approach to integration across existing security tools provides a key competitive advantage. Rather than requiring organizations to replace their current security stack, Prophet integrates with existing Security Information and Event Management (SIEM) systems, Endpoint Detection and Response (EDR) platforms, and other security tools.

“If you’ve got to go get five or six different copilots to use within your organization, it’s going to be very confusing,” Shah explained. “What customers are telling us is that, hey, I want an independent AI SOC platform that can help me triage, investigate and respond to alerts from all of my security tools, not just one or two.”

Accel’s preemptive investment signals growing confidence in autonomous security systems

Eric Wolford, Partner at Accel, emphasized the combination of technical innovation and proven market traction that drove the investment decision. “What stood out to us about Prophet wasn’t just the technical ambition, but the real-world traction: they’re delivering autonomy and speed while showing their work—a critical differentiator in an industry that runs on trust,” Wolford said in a statement.

Accel’s cybersecurity investment portfolio includes CrowdStrike, Tenable, and BlackPoint Cyber, providing the firm with deep expertise in evaluating security technologies. The preemptive nature of the funding round — Prophet was not actively seeking capital — underscores investor confidence in the company’s trajectory.

The funding will primarily support engineering expansion and go-to-market acceleration as Prophet scales its platform capabilities. The company plans to continue expanding its agentic AI platform, potentially adding new modules for additional security operations workflows.

Industry experts predict widespread adoption of AI agents will reshape cybersecurity landscape

Prophet’s success reflects broader trends reshaping cybersecurity. Deloitte’s 2025 cybersecurity forecasts predict widespread adoption of agentic AI systems, with 40% of large enterprises expected to deploy such systems in their SOCs by 2025. The consulting firm characterizes this shift as moving from “automation that follows instructions to automation that thinks.”

The company’s “role elevation” philosophy — enhancing rather than replacing human analysts — addresses concerns about AI displacing cybersecurity professionals. Shah emphasized that automation should free analysts from repetitive tasks to focus on higher-value security work.

“This is not about eliminating jobs,” Shah said. “It’s about ensuring an analyst doesn’t have to spend time triaging and investigating alerts, because who wants to do that all day, every day? Instead, they can focus on the 4% of issues that truly matter to an organization. They’re advancing their careers and doing more higher-order security work.”

As cyber threats continue evolving and incorporating AI capabilities, the arms race between attackers and defenders increasingly relies on technological sophistication rather than human capacity alone. Prophet’s approach suggests a future where cybersecurity becomes primarily a contest between AI systems, with human expertise focused on strategic oversight and complex decision-making.

The company’s ability to demonstrate measurable improvements in SOC efficiency while maintaining transparency and explainability positions it to capture market share as organizations grapple with the dual pressures of increasing threats and persistent talent shortages. With the new funding, Prophet Security aims to accelerate this transition, potentially setting the standard for how organizations defend against AI-powered attacks in an era where the speed and scale of threats exceed human capacity to respond manually.

But perhaps the most telling indicator of this shift isn’t Prophet’s technology or funding — it’s what happened when Shah’s team wasn’t actively seeking investment. Accel approached them anyway, recognizing that in a world where attackers launch AI-powered assaults at machine speed, the old playbook of human-driven defense isn’t just insufficient — it’s obsolete.

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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.

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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

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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

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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

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