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Enterprises must rethink IAM as AI agents outnumber humans 10 to 1

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more Stolen credentials are responsible for 80% of enterprise breaches. Every major security vendor has converged on the same conclusion: Identity is now the control plane for AI security. Scale alone demands this […]

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more


Stolen credentials are responsible for 80% of enterprise breaches. Every major security vendor has converged on the same conclusion: Identity is now the control plane for AI security. Scale alone demands this shift. Enterprises managing 100,000 employees will handle more than one million identities when AI agents enter production.

Traditional identity access management (IAM) architectures can’t scale to secure the proliferation of agentic AI. They were built for thousands of human users, not millions of autonomous agents operating at machine speed with human-level permissions. The industry response represents the most significant security transformation since the adoption of cloud computing.

Proximity-based authentication replaces hardware tokens

Leading vendors now use Bluetooth Low Energy (BLE) between devices and laptops to prove physical proximity. Combined with cryptographic identities and biometrics, this creates four-factor authentication without the need for hardware tokens.

Cisco’s Duo demonstrates this innovation at scale. Their proximity verification delivers phishing-resistant authentication using BLE-based proximity in conjunction with biometric verification. This capability, unveiled at Cisco Live 2025, represents a fundamental shift in authentication architecture.

Microsoft’s Entra ID handles 10,000 AI agents in single pilot programs while processing 8 billion authentications daily. “Traditional directory services weren’t architected for autonomous systems operating at this velocity,” states Alex Simons, CVP of identity at Microsoft.

Ping Identity’s DaVinci orchestration platform pushes further. The system processes more than 1 billion authentication events daily, with AI agents accounting for 60% of the traffic. Each verification completes in under 200 milliseconds while maintaining cryptographic proof.

Behavioral analytics catch compromised agents in real time

CrowdStrike treats AI agents like any other identity threat. Their Falcon platform establishes behavioral baselines for each agent within 24 hours. Deviations trigger automated containment within seconds.

“When an AI agent suddenly accesses systems outside its established pattern, we treat it identically to a compromised employee credential,” Adam Meyers, head of counter adversary operations at CrowdStrike, told VentureBeat. The platform tracks 15 billion AI-related events daily across customer environments.

That speed matters. CrowdStrike’s 2025 Global Threat Report documents that adversaries are achieving initial access in less than 10 minutes. They move laterally across 15 systems within the first hour. AI agents operating with compromised identities amplify this damage exponentially.

Identity resilience prevents catastrophic failures

Enterprises average 89 different identity stores across cloud and on-premises systems, according to Gartner. This fragmentation creates blind spots that adversaries exploit daily. The fix applies networking principles to identity infrastructure.

Okta’s Advanced Server Access implements redundancy, load balancing and automated failover across identity providers. When primary authentication fails, secondary systems activate within 50 milliseconds. This becomes mandatory when AI agents execute thousands of operations per second.

“Identity is security,” Todd McKinnon, CEO of Okta, said at Oktane 2024. “When you move AI into production, you give agents access to real systems, real data and your customer data. One compromised agent identity cascades across millions of automated actions.”

Zero trust scales for agent proliferation

Palo Alto Networks’ Cortex XSIAM completely abandons perimeter defense. The platform operates on the assumption of continuous compromise. Every AI agent undergoes verification before each action, not just at initial authentication.

Mike Riemer, Field CISO at Ivanti, reinforced the zero trust approach in a recent interview with VenturBeat: “It operates on the principle of ‘never trust, always verify.’ By adopting a zero trust architecture, organizations can ensure that only authenticated users and devices gain access to sensitive data and applications.”

Cisco’s Universal ZTNA extends this model to AI agents. The platform expands zero trust beyond humans and IoT devices to encompass autonomous AI systems, providing automated discovery and delegated authorization at scale.

Automated playbooks respond instantly to identity anomalies. When malware triggers authentication irregularities, XSIAM revokes access and launches forensic analysis without human intervention. This zero-latency response becomes the operational baseline.

Zscaler CEO Jay Chaudhry identified the core vulnerability at Zenith Live 2025: “Network protocols were designed to allow trusted devices to communicate freely. AI weaponizes this legacy architecture at scale. Adversaries craft phishing campaigns that compromise agent identities faster than humans can respond.”

Universal ZTNA frameworks enable million-agent deployments

The architectural requirements are clear. Universal zero trust network access (ZTNA) frameworks across the industry provide four capabilities essential for AI environments.

Cisco’s implementation demonstrates the scale required. Their Universal ZTNA platform performs automated discovery scans every 60 seconds, cataloging new AI deployments and permission sets. This eliminates blind spots that attackers target. Cisco’s delegated authorization engine enforces least-privilege boundaries through policy engines processing 100,000 decisions per second.

Comprehensive audit trails capture every agent action for forensic investigation. Security teams using platforms like Cisco’s can reconstruct incidents across millions of interactions. Native support for standards like the Model Context Protocol ensures interoperability as the ecosystem evolves.

Ivanti’s approach complements these capabilities with AI-powered analytics. Daren Goeson, SVP of product management at Ivanti, emphasizes: “AI-powered endpoint security tools can analyze vast amounts of data to detect anomalies and predict potential threats faster and more accurately than any human analyst. These tools provide clear visibility across devices, users and networks, proactively identifying potential security gaps.”

Cisco’s AI security architecture sets industry direction

Cisco’s AI Secure Factory positions them as the first non-Nvidia silicon provider in Nvidia’s reference architecture. By combining post-quantum encryption with new devices, Cisco is building infrastructure to protect against threats that don’t yet exist. The enterprise takeaway: Securing AI isn’t optional; it’s architectural.

At Cisco Live 2025, the company unveiled a comprehensive identity and AI security strategy that addresses every layer of the stack:

AnnouncementCore problem solved / strategic valueTechnical detailsAvailability
Hybrid mesh firewall (incl. HyperShield)Distributed, fabric-native security; moves security from the perimeter into the network fabriceBPF-based enforcement; hardware accelerationNew firewalls: Oct 2025
Live protectCloses “45-day patch vs. 3-day exploit” gap with rapid, kernel-level vulnerability shieldingReal-time patching without rebootsNexus OS: Sept 2025
Splunk: Free firewall log ingestionReduces SIEM costs up to 80%; incentivizes Cisco firewall adoptionUnlimited log ingestion from Cisco firewallsAug 2025
Splunk: Observability for AIProvides critical visibility into AI stack performanceMonitors GPU utilization and model performanceSept 2025
Duo IAMEvolves from MFA to a complete security-first IAM platformUser Directory, SSO, Identity Routing EngineAvailable Now
Duo: Proximity verificationDelivers phishing-resistant authentication without hardware tokensBLE-based proximity, biometric verificationPart of the new Duo IAM
Duo: Identity resilienceAddresses critical IDP outage risksRedundancy, load balancing and automated failoverIn development
Cisco universal ZTNAExpands zero trust to humans, IoT/OT devices and AI agentsAutomated discovery, delegated authorizationOngoing evolution
Open-sourced security AI modelDemocratizes AI defense; 8B parameters match 70B model performanceRuns on CPU; 5B security tokens trainingAvailable (Hugging Face)
AI defense and Nvidia partnershipSecures AI development pipelineNvidia NIM microservices optimizationAvailable now
Post-quantum securityFuture-proof against quantum attacksMACsec and IPsec encryptionNew devices (June 2025)
Identity intelligenceContinuous behavioral monitoringAI-powered anomaly detectionPart of Security Cloud
Secure accessConverges VPN and ZTNA capabilitiesCloud-delivered secure access service edgeAvailable now

Cross-vendor collaboration accelerates

The Cloud Security Alliance Zero Trust Advancement Center now includes every major security vendor. This unprecedented cooperation enables unified security policies across platforms.

“Security vendors must unite against common threats,” George Kurtz, CEO of CrowdStrike, emphasized during a recent platform strategy discussion. “The data-centric approach wins given how fast adversaries and threats evolve.”

Cisco President and CPO Jeetu Patel echoed this sentiment in an interview with VentureBeat: “Security is a prerequisite for adoption of AI. If people don’t trust the system, they’re not going to use it.”

The organizational challenge remains. Robert Grazioli, CIO at Ivanti, identifies the critical barrier: “CISO and CIO alignment will be critical in 2025. This collaboration is essential if we are to safeguard modern businesses effectively. Executives need to consolidate resources — budgets, personnel, data and technology — to enhance an organization’s security posture.”

The identity reckoning

When Cisco, Okta, Zscaler, Palo Alto Networks and CrowdStrike independently reach identical conclusions about identity architecture, it’s confirmation, not coincidence.

Identity infrastructure determines security outcomes. Organizations face two options: Architect identity as the control plane or accept breaches as inevitable. The gap between AI deployment speed and identity security maturity narrows daily.

Three actions cannot wait. Audit every AI agent’s identity and permissions within 30 days. Deploy continuous verification for all non-human identities immediately. Establish 24/7 identity security operations to prevent adversaries from exploiting gaps.

The vendor consensus sends a clear and unmistakable signal. Identity has become the control plane for AI security. Enterprises that fail to adapt will spend 2025 managing breaches instead of innovation.

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Amazon’s Project Rainier Sets New Standard for AI Supercomputing at Scale

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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.

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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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