Red Hat and CrowdStrike say the real AI race is no longer about model performance alone, but about control and the orchestration required to exercise it.
Red Hat and CrowdStrike say that the real AI race is no longer about model performance alone, but about control and the orchestration required to exercise it. One of technology’s most transformative ideas began with a deceptively simple question: what if anyone could see, shape, and share the code that runs the world? That question gave birth to open source, rewiring the internet and redefining how the cloud works.
Now AI is approaching a similar turning point, where the world must decide whether this technology will remain closed and controlled by a few or become open, inspectable, and shaped by a broader community. On paper, it seems easy to reuse the familiar formula: open the models, let the community improve them, and watch innovation take off. But AI is not just code. It learns, judges, adapts and acts, which means its behavior cannot simply be inspected or controlled the way software can. That makes the path to ‘openness’ far more complicated, as transparency must extend to understanding how the system evolves, where decisions come from and how those decisions can be audited or orchestrated at scale. Policymakers and developers are also wrestling with what openness should really mean and whether enterprises can trust AI systems they cannot inspect or audit. The White House’s, released in July, urges federal agencies to foster environments where open models can thrive. In August, OpenAI echoed that momentum through itsThe development exposed a deeper question for enterprise AI: Is this really the limit of transparency, or should organizations demand something far greater? For Red Hat’s CEO Matt Hicks, moves like OpenAI’s open-weights release signal progress, but they still fall short of what true open source AI demands. He argues that openness cannot stop at model weights, because sharing them does not automatically create transparency or control. In AI, the weights or numeric values a model learns during training define how the system interprets inputs, makes decisions and adjusts its behavior. Hicks believes that if enterprises can’t probe, rerun, or modify the systems they rely on, they aren’t actually in control of their AI, and the open-source promise remains unfulfilled. “In a perfect world, all training data plus model weights would be open source. This means standardized, open datasets used to train models across the board, along with basic parameters for model weights. But we aren’t there yet,” he said. Until then, he believes the definition of openness should begin with model weights, but be further supported by an open ecosystem of tools and platforms that prevent vendor lock-in. That, he said, is the baseline for genuine open-source AI.“We’re already seeing many of the largest players in the model space realize that open source is a path forward, if not the path forward. But it can’t just be the models - we also need to pay attention to open source efforts around the tools used to build models and AI-enabled applications, the platforms that they run on, inference servers and more,” said Hicks. As organizations adopt systems that can reason, adapt, and act, the challenge is shifting from seeing what AI does to coordinating how it behaves. That shift is why orchestration is also becoming indispensable. In practice, orchestration helps a system decide which model handles which task, sequences actions across them, manages compute resources and hands decisions to humans when judgment is required. Moreover, it prevents AI from devolving into a tangle of disconnected automation. No field feels this pressure more than cybersecurity, where even small lapses in visibility or control can become full-blown breaches in minutes. Cybersecurity giant CrowdStrike recently measured the fastest attacker breakout time, the interval between initial compromise and lateral movement, at justThe company’s response is to move cybersecurity beyond automation, toward what it calls an ‘agentic Security Operations Center ’, a fleet of specialized “SOC workflows demand multiple domain-specific tasks executed with speed and precision. As adversaries increasingly leverage AI to scale and accelerate attacks, one-size-fits-all large, monolithic AI models simply can’t keep up,” Michael Sentonas, president of CrowdStrike, told me. “Agentic SOC enables analysts to orchestrate an ensemble of agents, each focused on a distinct task but working in concert to automate high-impact workflows like threat hunting, detection triage, and response.”At the center of CrowdStrike’s agentic SOC strategy sits Charlotte AI, a security assistant trained on millions of analyst decisions. The company claims that Charlotte now matches human conclusions with roughly 98% accuracy across many workflows and can reclaim about 40 hours of manual effort per week by taking over repetitive investigation and triage. Charlotte Agentic SOAR, CrowdStrike’s security orchestration, automation, and response system, acts as the connective tissue between Charlotte, an expanding fleet of specialized agents, and the real-world workflows they support. Instead of treating each agent as an isolated tool, Charlotte Agentic SOAR lets customers chain agents together into multi-step investigations and responses. A detection agent can trigger a triage agent, which can in turn call a remediation agent, all under human-defined guardrails. Charlotte AI can supervise, reason about context and escalate to analysts when judgment calls arise. “Instead of following predefined steps, agents dynamically reason through conditions the human author could never fully predict. Analysts define the mission; the agents share context, collaborate, and determine the next best action—all under human oversight. It delivers a step change in speed and precision, giving defenders back the most valuable currency in cybersecurity: time,” explained Sentonas. The security sector brings a different lens to the open source debate. Closed AI systems do not just limit innovation; they hide risk. If defenders can’t see how models interact with other tools, they miss chances to catch prompt attacks, exfiltration routes or manipulated behavior. Agentic AI is offering a way to counter that opacity, as autonomous agents can watch systems in real time, flag anomalies, and log every action. “As organizations adopt the agentic SOC, several disciplines become critical. Data quality and hygiene are foundational, ensuring agents have the right information to make the right decisions,” said Sentonas. “A culture of data-driven decision-making and risk management must guide how teams interpret and act on machine outputs. And strong governance, deciding what to automate, when, and to what degree, will define how safely and effectively enterprises scale agentic operations.”. The company says its Falcon platform is delivering the AI-driven outcomes SOC teams need, with the quarter generating $265 million in net new annual recurring revenue, up 73% year over year, and driving total ARR to $4.92 billion, alongside $398 million in operating cash flow and $296 million in free cash flow.The philosophies of Red Hat and CrowdStrike point to a shared view of where open-source AI must go next. The future lies in a hybrid model: open, inspectable systems paired with governed, agentic orchestration. Openness without orchestration creates visibility into systems that still cannot be managed at scale. Orchestration without openness risks powerful, unaccountable machines that answer only to their creators. Neither outcome serves enterprises, regulators, or the public. “The model doesn’t create the ecosystem; it’s just an aspect of it. There are tools, platforms, inference servers, and much more that form a complete AI stack, all of which also need to be driven by open source innovation,” said Hicks. “Enterprises need to look at AI as an extension of the hybrid cloud.” He added that open-source AI, from the models to the tools to the platforms, gives organizations the same choice and control that open-source software brought to cloud computing. “The greater accessibility of open source allows for this creativity, helping you to take an existing model and modify it to your specific needs—just like you can do with open source software right now,” Hicks said. The real AI arms race may soon be less about building bigger models and more about building systems that are trustworthy and defensible: open by design, orchestrated in practice, and capable of operating at enterprise speed and scale. How that vision shapes the future of open-source AI in 2026 remains to be seen.
AI Orchestration Red Hat Crowdstrike Crowdstrike Agentic SOC Red Hat AI AI Cybersecurity Enterprise AI 2026 Open-Weights Models Open AI Gpt Oss
United States Latest News, United States Headlines
Similar News:You can also read news stories similar to this one that we have collected from other news sources.
FIRST ALERT: Hurricane force winds and extreme snowToday's Alaska weather forecast from Alaska's Weather Source.
Read more »
100 kidnapped Nigerian schoolchildren released: UN sourceThe released children are set to be handed over to local government officials in Niger state, UN source says.
Read more »
FIRST ALERT: First heavy snow makes landfall in Southeast, setting a new record in JuneauToday's Alaska weather forecast from Alaska's Weather Source.
Read more »
Source: Texans signing veteran tight end Brevin Jordan to one-year deal for 2026Texans extend injured tight end
Read more »
Oprah Pursues Dr. Phil On Ship Through ArcticAmerica’s Finest News Source
Read more »
JD Vance Reminded To Use White House Service EntranceAmerica’s Finest News Source
Read more »