Latest news with #Gigamon
Forbes
2 days ago
- Business
- Forbes
Responsible AI Starts With The C-Suite
Shane Buckley is President and Chief Executive Officer of Gigamon, a leader in deep observability. AI is at the top of every board agenda today. With global AI investment expected to surpass $200 billion in 2025 and $750 billion by 2028, the central conversation has shifted to balancing innovation with responsible data use. As AI matures, so do its risks—particularly those related to privacy, security and ethics. To scale responsibly, business leaders must find equilibrium between aggressive AI adoption and intentional governance. As I emphasized in my last Forbes Technology Council article, AI budgeting must begin with a security-first mindset. Today, that mindset is no longer optional. It's a strategic imperative for the C-suite—one that sets the foundation for sustainable, scalable success. Data Risk Is The Core Challenge At the center of AI-related risk lies a fundamental uncertainty: how large language models (LLMs) process, retain and expose sensitive data. These models often contradict Zero Trust principles by opening broader access to networks and information. When confidential business data is entered into third-party AI tools, it may be stored in jurisdictions with incompatible compliance or privacy laws. Employee and third-party misuse—intentional or not—can expose organizations to data leakage, regulatory risk or public breach. According to my company Gigamon's 2025 Hybrid Cloud Security Survey, which included over 1,000 IT and security leaders, visibility into data in motion is now a top business priority. More than half (54%) of respondents expressed reluctance to use AI in public cloud environments due to intellectual property risks, while seven in ten are considering moving data from public to private clouds. The Internal Threat Is Often Overlooked While headlines focus on deepfakes and AI-enabled phishing attacks, a quieter threat looms within: employees unknowingly inputting sensitive data into unsecured AI tools. Even well-intentioned teams can become the weakest link if the organization lacks appropriate controls. A Growing Dark Market For Malicious AI On the dark web, malicious AI tools—black-hat versions of ChatGPT—are enabling adversaries to launch more frequent and sophisticated attacks. Our survey found that 58% of leaders observed an increase in AI-powered ransomware. In 2024 alone, there was an 11% increase global spike in reported ransomware attacks, with over 5,400 attacks logged. The convergence of an expanding threat surface and rapidly advancing attacker capabilities makes a reactive cybersecurity strategy untenable. Accountability Must Extend To Vendors As AI use becomes embedded in third-party systems, vendors represent a growing risk surface. Increasingly, companies are requiring detailed disclosures on how their partners use AI. Transparency, accountability and aligned standards across vendors are critical. If even one supplier is compromised, AI-powered malware can cascade through interconnected systems and impact entire supply chains. Business leaders must extend security-first thinking to external partnerships and vendor ecosystems. When AI is accessible without oversight, organizations risk losing control over their data footprint. But bans aren't the answer—these only encourage unmonitored "shadow AI" use. Instead, responsible enablement must prevail. That includes educating employees, enforcing clear policies and building visibility across enterprise AI stack. Boards Must Lead Governance AI governance is no longer the sole domain of IT. It's a board-level issue. Forward-looking organizations are forming AI governance committees that include the CEO, CISO, CRO and General Counsel. These cross-functional teams are tasked not only with risk oversight, but also with defining the organization's risk appetite, monitoring AI use and maintaining compliance across jurisdictions. True governance is more than policy—it's cultural. It ensures AI is used safely but applied in ways that benefit both people and the business. Ethical Risks Carry Legal Consequences Security isn't the only concern. AI systems can carry ethical reputational risks—from bias to misinformation. Bias in AI can lead to discriminatory results. Hallucinations, when models generate convincing but false information, can mislead decision-making and create legal exposure. Organizations can mitigate these risks through measures like pseudonymization—removing personally identifiable information (PII) before inputting the data into AI systems. Even simple steps—such as stripping customer or vendor names—can improve privacy protection and reduce harmful outcomes. Prioritize Responsible Innovation AI has transformative potential, but only for organizations that wield it with care. C-suite leaders must guide their organizations through bold innovation while safeguarding core values—people, data and trust. Those who take a security-first, governance-led approach today will shape the AI-powered businesses of tomorrow. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Scoop
6 days ago
- Business
- Scoop
Gigamon Launches AI Tools For Deep Observability
Multi-phase AI strategy delivers intelligent visibility and automation, sets a new standard for hybrid cloud security and management Gigamon, a leader in deep observability, today announced the first phase of its multi-year AI strategy, introducing foundational innovations designed to help organizations better secure and manage hybrid cloud infrastructure. The initial offerings include Gigamon AI Traffic Intelligence, which delivers real-time visibility into GenAI and LLM traffic across 17 leading engines to enable data-driven enforcement and policy governance, and GigaVUE Fabric Manager (FM) Copilot, a GenAI-powered assistant that simplifies onboarding, configuration, management, and troubleshooting of Gigamon deployments. By embedding AI into the Deep Observability Pipeline, Gigamon expands its value to customers by eliminating blind spots, strengthening governance, and enhancing operational efficiency across modern hybrid environments. We're embedding AI directly into the Deep Observability Pipeline to help customers strengthen cybersecurity with practical, easy-to-implement capabilities that keep pace with the speed and complexity of AI adoption. As GenAI workloads multiply, organizations face surging data volumes, expanding attack surfaces, and growing security risks. One of the most fundamental challenges is simply knowing which AI services are in use. In the 2025 Hybrid Cloud Security Survey of over 1,000 global Security and IT leaders, one in three reported that network traffic has more than doubled due to AI workloads, while 55 percent said their tools are failing to detect modern threats. In response, 88 percent now consider deep observability—combining network-derived telemetry with log data—essential for securing and scaling AI deployments across hybrid cloud infrastructure. 'As GenAI use matures in organizations, we're focused on both AI for security and security for AI,' said Michael Dickman, chief product officer at Gigamon. 'It has never been more true that you cannot secure what you cannot see, making complete visibility into AI traffic and workloads, including shadow AI usage, critical for today's Security and IT teams. That is why we're embedding AI directly into the Deep Observability Pipeline to help customers strengthen cybersecurity with practical, easy-to-implement capabilities that keep pace with the speed and complexity of AI adoption.' Complete Visibility into AI and GenAI Network Traffic: A New Standard for Cybersecurity The Gigamon Deep Observability Pipeline efficiently delivers actionable network-derived telemetry, including packets, flows, and application metadata directly to cloud, security, and observability tools, bringing the complete picture into focus. With the new AI Traffic Intelligence capability, organizations gain real-time visibility into GenAI and LLM activity from 17 leading engines, including ChatGPT, Gemini, and DeepSeek. The capability also allows user-defined targeting of additional LLMs beyond the pre-defined set, extending flexibility and reach. For ease of integration, this intelligence is agentless and applies even to encrypted data in motion, surfacing shadow AI usage and enabling more effective, policy-driven governance. AI Traffic Intelligence enables organizations to: Gain real-time insights into GenAI and LLM traffic across public, private, virtual, and container environments Identify shadow AI, or unsanctioned AI usage, to reduce risk and improve oversight Track usage patterns to inform governance and manage AI-related costs Empower Security and IT teams with trusted, network-derived telemetry to drive informed decisions 'Gigamon has established itself as a trusted source of granular network data, providing comprehensive visibility across highly complex, distributed environments,' said Bob Laliberte, principal analyst at theCUBE Research. 'As AI increases the complexity and volume of network traffic, clear visibility into GenAI activity has become critical. Gigamon is well-positioned to meet these emerging challenges by delivering the requisite insights to monitor AI usage, regain control, and take decisive action.' 'AI is accelerating digital transformation, but it's also introducing security risks and data challenges across hybrid cloud infrastructure," said Chris Konrad, vice president, Global Cyber at World Wide Technology (WWT). 'By integrating AI into its Deep Observability Pipeline, Gigamon delivers the complete visibility and insights our customers need to detect threats, govern GenAI use, and strengthen cybersecurity best practices. At WWT, we're proud to partner with Gigamon to shape the future of hybrid cloud security by delivering the deep observability customers require.' GigaVUE-FM Copilot Simplifies Deployment and Day-to-Day Operations Gigamon also introduced GigaVUE-FM Copilot, a GenAI-powered assistant designed to help organizations onboard, configure, manage, and troubleshoot their Gigamon environments with greater speed and accuracy. Embedded directly within GigaVUE-FM, GigaVUE-FM Copilot enables Security and IT teams to reduce time to insight, simplify complex workflows, and improve productivity. Through a natural language interface, GigaVUE-FM Copilot securely connects users directly to the internal knowledge base and LLM contained within technical documentation, deployment guides, and release notes, delivering fast, context-aware answers. This capability empowers Security, IT, and DevOps teams to resolve issues independently, whether or not they are power users, and reduce reliance on Tier 3 support resources. With GigaVUE-FM Copilot, organizations can: Simplify configuration and management using GenAI-assisted support Accelerate onboarding and feature discovery to improve readiness Instantly search documentation to troubleshoot and apply best practices Reduce Tier 3 support escalations by enabling broader self-service Improve operational efficiency across teams and environments Availability and Roadmap The AI Traffic Intelligence capability is available now for all GigaVUE Cloud Suite customers. GigaVUE-FM Copilot is in early access for select customers, with general availability in 2H25. Additional AI-powered innovations are underway as part of the multi-phase strategy and will be spotlighted at the Gigamon Visualyze Bootcamp, the company's virtual customer conference taking place Sept. 9–11. For more information
Techday NZ
7 days ago
- Business
- Techday NZ
Gigamon set to lead deep observability with 52 percent share by 2025
New research from Frost & Sullivan reveals that Gigamon is projected to command a 52 percent share of the global deep observability market in 2025, as organisations place a greater emphasis on securing hybrid cloud infrastructure. Frost & Sullivan's analysis, commissioned by Gigamon, estimates the total addressable market for deep observability will reach USD $880 million in 2025 and expand to USD $2.7 billion by 2029, representing a compound annual growth rate of 33 percent. Market drivers The study highlights that growing adoption of hybrid cloud, increased threat complexity, and the proliferation of artificial intelligence (AI) workloads are key factors driving demand for deep observability solutions. As the number and sophistication of attacks increases, traditional log-based security tools are viewed as insufficient for protecting distributed environments. According to the recent Gigamon 2025 Hybrid Cloud Security Survey of over 1,000 global security and IT leaders, real-time monitoring and visibility across all data in motion are now the top priorities for modern defence strategies. Nearly 89 percent of respondents agreed that deep observability is foundational to effective cloud security. Definition and benefits Frost & Sullivan defines deep observability as the efficient provision of network-derived telemetry to cloud, security, and observability tools. Unlike traditional log analytics, deep observability enhances visibility across complex, hybrid architectures by leveraging detailed insights from network traffic rather than solely relying on pre-existing data logs. The research states that this approach allows security and IT teams to gain a comprehensive view of network and application performance, which in turn can improve security postures and reduce risk by identifying otherwise undetected threats and vulnerabilities. "Over the past year we've seen organisations increasingly prioritise visibility into all data in motion, as they seek to secure their hybrid cloud environments against an accelerating threat landscape," stated Vinay Biradar, Associate Director, Cybersecurity Advisory at Frost & Sullivan. "The increasing complexity of dynamic and distributed workloads is driving a shift in security investments toward solutions that help deliver complete visibility and reduce risk. Our research once again highlights Gigamon as the industry leader, due to its Deep Observability Pipeline and vast ecosystem, as it delivers the rich network-derived telemetry that modern security tools need to effectively secure data and infrastructure from evolving cyberthreats." Sector adoption and drivers Uptake is especially strong among large enterprises with more than 5,000 employees and US Federal Agencies, owing partially to mandatory requirements relating to Zero Trust architectures. The research found that the US Federal government exhibits the highest adoption rate within its sector due to compliance with Zero Trust regulations. Other reported drivers for adopting deep observability solutions include operational efficiency, cost reduction, improved compliance and governance, and the need for comprehensive insight into network traffic, particularly as organisations deploy new AI workloads at scale. Shane Buckley, President and CEO at Gigamon, commented on the evolving technology landscape: "AI is upping the ante for organisations, making complete visibility into all data in motion even more challenging across hybrid cloud infrastructure as organisations rapidly deploy new AI workloads. Increasingly, our customers are relying on the network-derived telemetry we deliver across their virtual machines, containers, cloud, and physical infrastructure, to help eliminate blind spots and vulnerabilities where threat actors could hide. The continued validation of deep observability as a rapidly growing market category underscores its significance in modern cybersecurity tech stacks." Study methodology Frost & Sullivan's research was conducted through a top-down analysis of the deep observability market. This included estimates of the number of large global enterprises and US federal agency adoption rates, as well as typical enterprise spending on deep observability solutions. The findings were derived from both Frost & Sullivan's proprietary research and primary interviews with market participants, including Gigamon.
Scoop
26-06-2025
- Business
- Scoop
Gigamon Leads Expanding Deep Observability Market With 52 Percent Market Share In 2025 - New Frost & Sullivan Research
Gigamon, a leader in deep observability, has been recognized as a leading vendor in the high-growth deep observability market, according to new research by Frost & Sullivan commissioned by Gigamon. Overall, the deep observability total addressable market (TAM) is estimated at $880 million in 2025, growing to $2.7 billion in 2029, representing a compound annual growth rate (CAGR) of 33 percent as organizations increasingly embrace hybrid cloud infrastructure, according to the study. Amid today's evolving threat landscape, traditional log data from cloud, security, and observability tools is no longer sufficient in securing and managing complex hybrid cloud infrastructure. In the recently published Gigamon 2025 Hybrid Cloud Security Survey of more than 1,000 global Security and IT leaders, real-time threat monitoring and visibility across all data in motion was named as the top priority to optimize defense-in-depth strategies. As a result, nearly 9 in 10 (89 percent) Security and IT leaders agreed that deep observability is a foundational element of cloud security. Deep Observability Delivers Complete Visibility, Cost Efficiencies for Hybrid Cloud Infrastructure Frost & Sullivan defines deep observability as the ability to efficiently deliver network-derived telemetry to cloud, security, and observability tools. Emerging from the traditional observability market, the deep observability market has matured into a critical capability for organizations, according to the report. The ability to augment traditional log data with network-derived telemetry and insights enables Security and IT teams to gain complete visibility across hybrid cloud infrastructure, improving their overall security posture and optimizing network and application performance, according to the research. 'Over the past year we've seen organizations increasingly prioritize visibility into all data in motion, as they seek to secure their hybrid cloud environments against an accelerating threat landscape," stated Vinay Biradar, associate director, Cybersecurity Advisory at Frost & Sullivan. "The increasing complexity of dynamic and distributed workloads is driving a shift in security investments toward solutions that help deliver complete visibility and reduce risk. Our research once again highlights Gigamon as the industry leader, due to its Deep Observability Pipeline and vast ecosystem, as it delivers the rich network-derived telemetry that modern security tools need to effectively secure data and infrastructure from evolving cyberthreats.' According to the research, the global deep observability market is significantly influenced by the increasing adoption rates among large enterprises (5,000+ employees) and US Federal Agencies, which have the highest adoption rate within the US Federal government due to regulations around Zero Trust implementation. Key findings on factors that drive deep observability adoption in the AI-era include: Improving Security Posture Zero-Trust Architecture Implementation Operational Efficiency and Cost Reduction Improving Compliance and Cloud Governance Growing need for comprehensive network traffic insights 'AI is upping the ante for organizations, making complete visibility into all data in motion even more challenging across hybrid cloud infrastructure as organizations rapidly deploy new AI workloads," said Shane Buckley, president and CEO at Gigamon. "Increasingly, our customers are relying on the network-derived telemetry we deliver across their virtual machines, containers, cloud, and physical infrastructure, to help eliminate blind spots and vulnerabilities where threat actors could hide. The continued validation of deep observability as a rapidly growing market category underscores its significance in modern cybersecurity tech stacks.'
Yahoo
26-06-2025
- Business
- Yahoo
Gigamon Leads Expanding Deep Observability Market with 52 Percent Market Share in 2025, According to New Frost & Sullivan Research
Research highlights limitations of traditional security tools, stringent Zero Trust requirements, and expanding attack surface from AI workloads as drivers for deep observability adoption SANTA CLARA, Calif., June 17, 2025--(BUSINESS WIRE)--Gigamon, a leader in deep observability, has been recognized as a leading vendor in the high-growth deep observability market, according to new research by Frost & Sullivan commissioned by Gigamon. Overall, the deep observability total addressable market (TAM) is estimated at $880 million in 2025, growing to $2.7 billion in 2029, representing a compound annual growth rate (CAGR) of 33 percent as organizations increasingly embrace hybrid cloud infrastructure, according to the study. Amid today's evolving threat landscape, traditional log data from cloud, security, and observability tools is no longer sufficient in securing and managing complex hybrid cloud infrastructure. In the recently published Gigamon 2025 Hybrid Cloud Security Survey of more than 1,000 global Security and IT leaders, real-time threat monitoring and visibility across all data in motion was named as the top priority to optimize defense-in-depth strategies. As a result, nearly 9 in 10 (89 percent) Security and IT leaders agreed that deep observability is a foundational element of cloud security. Deep Observability Delivers Complete Visibility, Cost Efficiencies for Hybrid Cloud Infrastructure Frost & Sullivan defines deep observability as the ability to efficiently deliver network-derived telemetry to cloud, security, and observability tools. Emerging from the traditional observability market, the deep observability market has matured into a critical capability for organizations, according to the report. The ability to augment traditional log data with network-derived telemetry and insights enables Security and IT teams to gain complete visibility across hybrid cloud infrastructure, improving their overall security posture and optimizing network and application performance, according to the research. "Over the past year we've seen organizations increasingly prioritize visibility into all data in motion, as they seek to secure their hybrid cloud environments against an accelerating threat landscape," stated Vinay Biradar, associate director, Cybersecurity Advisory at Frost & Sullivan. "The increasing complexity of dynamic and distributed workloads is driving a shift in security investments toward solutions that help deliver complete visibility and reduce risk. Our research once again highlights Gigamon as the industry leader, due to its Deep Observability Pipeline and vast ecosystem, as it delivers the rich network-derived telemetry that modern security tools need to effectively secure data and infrastructure from evolving cyberthreats." According to the research, the global deep observability market is significantly influenced by the increasing adoption rates among large enterprises (5,000+ employees) and US Federal Agencies, which have the highest adoption rate within the US Federal government due to regulations around Zero Trust implementation. Key findings on factors that drive deep observability adoption in the AI-era include: Improving Security Posture Zero-Trust Architecture Implementation Operational Efficiency and Cost Reduction Improving Compliance and Cloud Governance Growing need for comprehensive network traffic insights "AI is upping the ante for organizations, making complete visibility into all data in motion even more challenging across hybrid cloud infrastructure as organizations rapidly deploy new AI workloads," said Shane Buckley, president and CEO at Gigamon. "Increasingly, our customers are relying on the network-derived telemetry we deliver across their virtual machines, containers, cloud, and physical infrastructure, to help eliminate blind spots and vulnerabilities where threat actors could hide. The continued validation of deep observability as a rapidly growing market category underscores its significance in modern cybersecurity tech stacks." About the Frost & Sullivan Deep Observability Research Frost & Sullivan conducted a top-down analysis of the Deep Observability Market by estimating the total number of large enterprises globally and US Federal Agencies' adoption of deep observability solutions, and the average spending of an enterprise on the solution. The data for the findings was gathered by Frost & Sullivan research as well as through primary interviews with market participants including Gigamon. To download the Frost & Sullivan Deep Observability market research, click here. About Gigamon Gigamon® offers a deep observability pipeline that efficiently delivers network-derived telemetry to cloud, security, and observability tools. This helps eliminate security blind spots and reduce tool costs, enabling you to better secure and manage your hybrid cloud infrastructure. Gigamon serves more than 4,000 customers worldwide, including over 80 percent of Fortune 100 enterprises, 9 of the 10 largest mobile network providers, and hundreds of governments and educational organizations. To learn more, please visit © 2025 Gigamon. All rights reserved. Gigamon and the Gigamon logo are trademarks of Gigamon in the United States and/or other countries. Gigamon trademarks can be found at All other trademarks are the property of their respective owners. View source version on Contacts Gigamon Media Contact: Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data
