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Orchid Security expands AI agent protection with readiness controls for identity governance and emergency shutdowns. Published: September 22, 2026 Readiness tagging, continuous observability, and application-level shutdown controls help enterprises scale AI agent deployments while maintaining control. New York, London - September 15, 2026 - Orchid Security today introduced identity drift detection and application-level kill switches for AI agents. The new controls address identity risks that can allow agents to quickly operate beyond their intended privileges by accessing unmanaged credentials, accounts, authentication paths, and excessive entitlements.The company's new AI readiness controls for AI agents are designed so that adoption can scale without control slipping away. Boards are making AI adoption a strategic imperative. Boards are focused on how quickly AI can be deployed, making it critical for security teams to enable adoption while keeping autonomous agents within sanctioned limits. "AI transformation is exciting. Identity hygiene is not," said Roy Katmor, co-founder and CEO of Orchid Security. "Boards are no longer asking whether AI will be adopted - they are asking why it is not moving faster, and security cannot answer with a blanket 'no.' Enterprises need to observe how agents act, understand when they drift, and govern them immediately, including terminating the authority through which they operate." Orchid's Identity Gap 2026 research found that 57% of enterprise identity remains unseen and unmanaged, creating access risks AI agents can exploit within seconds. A control-led model for deploying AI agents. Orchid delivers continuous AI readiness through four connected stages: Observe | Understand | Govern | Prove. The Identity Control Plane discovers agents and their access paths, measures runtime activity against approved scope, identifies identity hygiene and excessive entitlements, orchestrates responses when authority drifts, and creates an auditable record connecting agent actions to identities, applications, access paths, and governance actions. Available capabilities include AI readiness tagging, identity hygiene and security risk findings, ongoing drift detection, application-level kill switches, orchestrated credential and permission revocation, and audit generation. Orchid has also expanded its integration ecosystem with Palo Alto Networks Idira and Splunk Enterprise Security, allowing identity findings and telemetry to flow into the security infrastructure enterprises already use. Shannon Wilkinson, CIO and CISO at Findlay Automotive Group, said: "The challenge is how to enable the business to move faster and realize the productivity that AI agents bring, but it honestly terrifies a lot of us. At Findlay we're leaning heavily into AI to build a better customer experience. At the same time we must define guidelines, put guardrails in place and, above all, know what the identities are doing." For more on Orchid Security's approach to securing autonomous identities, or to request a demo, visit https://www.orchid.security/use-case/guardrails-for-autonomous-identity. About Orchid Security. Orchid Security provides an Identity Control Plane that delivers visibility, compliance, and control across enterprise applications. Its Identity-First Security Orchestration platform automatically discovers applications, analyzes authentication and authorization flows, and accelerates integration with identity governance systems. By uncovering and remediating hidden identity risks, Orchid helps enterprises reduce risk, lower operational costs, and achieve compliance at scale. Disclaimer Please be advised this section of cybernews.com features press releases to inform its audience about remarkable developments and announcements from various organizations. Cybernews.com shall not be held responsible for any claims, damages, or losses arising from using or relying on the information provided in the press release. By using this website, you acknowledge and accept the terms outlined in this Press Release Disclaimer. For further inquiries, please contact Cybernews here.

Seismic shift in Splunk pricing spotlights AI data management. The era of simply amassing heaps of raw telemetry is over, Splunk users say. Viable AI agents for automated IT ops will depend on putting all that data in context. * Beth Pariseau, Senior News Writer Published: 18 Sep 2026 DENVER - Splunk users stand to cut costs significantly with a recent pricing update, as raw telemetry collection gives way to the development of an advanced data architecture for AI. Splunk rolled out activity-based pricing in controlled availability last month with its Machine Data Lake (MDL), a new bulk storage layer within the Cisco Data Fabric (CDF) framework for AI data management. Activity-based pricing balances between ingest-based pricing, which is based on the volume of data indexed per day, and workload-based pricing, which charges for resources used during search and analytics workflows. Both existing pricing models require users to index all data upon ingestion, which incurs charges in both cases; activity-based pricing doesn't require that step until data is searched. The departure from universal indexing will have a major effect on how CIOs view Splunk, as AI data management becomes increasingly important to support autonomous agents, said Mike Leone, an analyst at Moor Insights & Strategy. "The cost savings will be enough that I could see some customers completely reevaluating what they're indexing the traditional Splunk way," Leone said. "A lot of Splunk customers have spent years architecting around ingest-based licensing, deciding what to index versus what to keep somewhere cheaper. Weighting search equally with ingest changes that math." Splunk: 'Add a zero' to data volumes at same cost. Splunk didn't disclose the specific cost of searches and indexing under activity-based pricing, but the goal is to let customers store orders of magnitude more data in MDL for the same price they paid previously, said Kamal Hathi, Splunk's senior vice president and general manager, in a keynote presentation at Splunk .conf26 this week. "What you need in this agentic era is to grow your data maybe 10x, without having your bill go up in proportion," Hathi said. "What we want to do is allow you to add a zero to the amount of data that you're processing while keeping your cost the same." Users can also use Federated Search to avoid indexing data until it's searched. Federated Search expanded this week to support Amazon CloudWatch and Databricks on AWS. Splunk officials also demonstrated a new Value Insights feature to help users assess which of Splunk's pricing options provide the most cost savings and said that an AI agent that optimizes storage tiering is in the works for CDF. While Federated Search is of interest, MDL potentially allows more data to be stored closer at hand for quicker access during incidents, said Karun Subramanian, senior director of AI for infrastructure and operations at UnitedHealth Group, during a panel presentation at this week's conference. "It's not just the access [to data]; it's the low-latency access, especially for agents in look-up analysis," Subramanian said. "You cannot really wait for an hour for data to be rehydrated [during an incident], and that is still a challenge with Federated Search." Leone said MDL has the potential to have a much more immediate effect on AI cost control than the tokenomics tools Splunk also introduced this week for its Agent Observability product. "They introduced so many different cost-saving techniques to a point where I could see customers getting a bit confused as to what would give them the biggest bang for their buck," he said. "A majority of the folks in the audience aren't there yet. They're still worried about rising costs and indexing everything. MDL is the natural fix almost immediately." Data management can make or break AI automation. Multiple presenters at Splunk .conf26 emphasized the importance of well-organized data to provide context and direction to autonomous agents. Constellation Energy, for example, uses AI features in Splunk's Agentic Security Operations Center (SOC) to counter mounting AI-driven attacks, according to a keynote presentation by Hossein Korsha, cybersecurity manager at the gas and electricity supplier headquartered in Baltimore. This AI-driven automation reduced the mean time to incident resolution for one application in Constellation's environment from 20 minutes to 39 seconds. "[With AI], we're able to ingest and observe data much faster, which lets us create detections much faster, find out vulnerabilities much faster and overall speed up our entire SOC response," Korsha said. Good data management has been integral to making AI automation work, he said. The company's earlier attempts to deploy Splunk's machine-learning-based Risk-Based Analytics (RBA) without well-curated data had an adverse effect. "We adopted RBA without having a mature enough asset and identity framework in Splunk, and it resulted in analysts taking in a lot of false positives, and made us step back, review our work, and start from the ground [up]," Korsha said. "Once we actually got everything mature enough, then we started seeing amazing results. And things are just getting better as we make adjustments." UnitedHealth's first attempt at building its own AI site reliability engineering (SRE) agent encountered similar issues due to fragmented data sources, Subramanian said during the panel presentation. "[We started with] a low-level bolt-on integration, a lot of customization, not really pretty, but that was the only way we could do it," he said. "Different API calls to different systems to pull the right data as needed, and, of course, then used the power of LLMs to come up with root cause analysis. Accuracy-wise, we started at 10%-15%... It's still not 80 or 90 that we would like it to be... but we're working on it." 'From raw telemetry to AI-ready signals' In a separate breakout presentation, Subramanian detailed the new data architecture required for an AI SRE agent to be most accurate - one that contrasts with the existing method of simply amassing extensive stores of metrics, logs and traces for observability. "AI is forcing us to rethink something much more fundamental than our tools. It's forcing us to rethink the data architecture," he said. "The shift that is required is going from raw telemetry to AI-ready signals." Moving to AI-ready signals requires an additional context layer that combines systems telemetry with operational context from configuration management databases, IT service management tools, business metadata, source code and runbooks, Subramanian said. Another term for this is data fabric, and the Cisco Data Fabric "can really give you a shortcut to get to [that]," he said. In addition to the MDL and Federated Search updates, Splunk shipped CDF components last month, including a data catalog and automation features for onboarding data into the Splunk Common Information Model format. A new Agent Launchpad no-code agent builder lets data analysts build agents that are triggered to take action in response to observability alerts. Subramanian indicated during that session and the data management panel that he's interested in moving toward CDF, though he declined to answer follow-up questions from TechTarget News following his presentation to confirm. In comments during the breakout presentation, Subramanian also cautioned that the CDF architecture is still new. "The context layer is up and coming," he said. "You won't find a lot of documentation around it... it's going to have a data catalog, knowledge graph and vector index, all to... get to the AI-ready signals stage." Log data, Cisco network tie-ins bolster Splunk. Virtually every observability, data management and even data storage vendor is vying to become the data fabric of choice for IT buyers, and most offer something similar to the context layer Subramanian described. But analysts say Splunk's long history of log management and analytics within big companies, combined with its integration of Cisco network topology, device health and event data into a new Network Intelligence App released this week, warrant serious consideration from IT buyers. "Splunk has security data and operational data, and increasingly, network data - that's a very unique data set," said Stephen Elliot, an analyst at IDC. "Add the new interface of Cisco Cloud Control, which is free with most Cisco purchases, and that enables [AI] context for various buyers - that's a very interesting approach to meeting different roles where they are." Beth Pariseau, senior news writer for Informa TechTarget, is an award-winning veteran of IT journalism. Have a tip? Email her or connect on LinkedIn. Related resources.

Cisco expands Splunk AI with NVIDIA. News | September 16, 2026 By Asma Adhimi Cisco is extending Splunk's AI capabilities to on-premises and air-gapped environments through a new infrastructure platform developed with NVIDIA. The company is also adding tools to track AI token spending and agent performance, while expanding Splunk's agentic security capabilities. For eeNews Europe readers, the developments are particularly relevant to organizations facing data sovereignty, security or regulatory constraints that make cloud-based AI difficult to deploy. They also highlight how AI infrastructure, observability and cybersecurity are increasingly being combined into a single enterprise stack. Splunk AI moves on-premises. Cisco AI POD for Splunk is the latest configuration within the Cisco Secure AI Factory with NVIDIA. Available now, it combines Cisco infrastructure, NVIDIA accelerated computing, AI runtime software and a Kubernetes-based architecture optimized for Splunk AI workloads. The platform allows Splunk Enterprise customers to run AI in their own data centers, private clouds and air-gapped environments instead of sending sensitive information to external cloud services. "One of the biggest roadblocks to enterprise AI today is that it's too hard to deploy," said Jeetu Patel, President and Chief Product Officer, Cisco. "Customers want to know: Can I trust it to do the job? Can I afford it? And, most importantly, can I secure it? By running Splunk AI on the infrastructure customers already trust, they can move faster to put AI to work in their business with confidence and control." Splunk AI Assistant is available on the platform now, while Agent Launchpad, designed for building custom AI agents, is due later this year. Customers will also be able to self-host several open and proprietary AI models, including Cisco's Deep Time Series Model, Google Gemma 4 and OpenAI GPT-OSS 20B. NVIDIA Nemotron open models are expected in the coming months. Tracking the cost of AI agents. Cisco is also expanding Splunk Agent Observability with a feature called Tokenomics, designed to show organizations how much their AI agents are costing in real time. The system tracks token spending across AI agents as well as coding tools including Claude Code, Codex and Cursor. Cisco says it will also forecast future consumption using its Deep Time Series Model, helping companies link AI usage and spending to business outcomes. Splunk Agent Observability is now available through Splunk Observability Cloud and Cisco Cloud Control. It monitors model and agent behavior across the AI stack and can apply runtime guardrails intended to prevent unsafe or inaccurate actions. More automation for security operations. Splunk is also expanding its Agentic SOC Workforce with AI agents for detection engineering, threat hunting, investigation, response and policy governance. New Exposure Analytics features add broader asset coverage, historical tracking and business-specific risk information. Separately, Splunk and AWS have signed a multi-year agreement to jointly develop security products aimed at AI-assisted detection, investigation and response. The companies plan to combine Splunk's security data and detection capabilities with AWS cloud infrastructure as enterprises move toward increasingly automated security operations. Linked Articles