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Techday NZ
a day ago
- Business
- Techday NZ
Teleport launches Secure MCP to protect AI enterprise workflows
Teleport has announced the general availability of its Secure Model Context Protocol (MCP) for use on the Teleport Infrastructure Identity Platform. The Secure MCP solution seeks to address new security challenges emerging from the rapid adoption of artificial intelligence across enterprises. Recent data from Enterprise Strategy Group indicate that 44% of enterprises have now deployed AI within their organisations. Teleport's Secure MCP is designed to provide security guardrails for AI systems as they interact with databases, MCP servers, and other forms of enterprise data. The Model Context Protocol is an open standard that enables AI models to connect with various tools, databases, or applications using a simplified, universal interface. This is intended to streamline integration in a manner akin to technology standards such as USB-C for physical devices. Despite these integration benefits, MCP was not originally intended with access control, which presents risks around unrestricted data access for AI models. Consequently, there is a need for mechanisms that can provide controlled, audited, and secure access to sensitive data. Teleport's Secure MCP responds to these needs by employing its Infrastructure Identity Platform, which extends existing trust frameworks to AI-based workflows. The platform enforces both Role-Based and Attribute-Based Access Controls (RBAC and ABAC) to manage the resources that large language models (LLMs) can access. Every session involving AI data access is logged, thereby contributing to regulatory compliance and audit readiness. Ev Kontsevoy, Chief Executive Officer of Teleport, commented on the development: "AI is terraforming how software is deployed in organizations. It shouldn't require a major public security incident to motivate business leaders to prepare for this impending challenge. Applying the same access control guardrails for AI, humans, and non-human identities accelerates AI adoption while locking in the protection needed to prevent unauthorized access of data. That's why we launched our secure MCP solution for Teleport, to enable enterprises to confidently unlock AI's innovation without falling prey to its security vulnerabilities and loopholes." Industry analysts have noted a concurrent rise in deployments of AI agents that operate within core enterprise systems, increasing the urgency for businesses to address identity and data security concerns. Todd Thiemann, Principal Analyst for Identity Security & Data Security at Enterprise Strategy Group, highlighted the pressing nature of these issues: "A wave of AI agent deployments that touch on core enterprise systems is in process, and identity teams need to be prepared. Recent Enterprise Strategy Group research showed that data privacy and security for AI agents were major concerns for enterprise security teams. Teleport's Secure MCP solution lays the groundwork for secure agent deployment and enables identity teams to get ahead of the game in securing their AI agent deployments." Secure MCP delivers several key architectural components for AI and MCP deployments. These include Zero Trust Networking, allowing only authenticated clients to interact with MCP servers over encrypted connections. A live MCP server inventory feature allows administrators to discover and register MCP tools across hybrid infrastructure environments automatically. Strict access control ensures that language models are only able to access resources for which they are specifically authorised, while the principle of least privilege means that authorisations are granted on a just-in-time basis for defined tasks. This minimises the potential risk of overprivileged or persistent access by AI models. Additionally, comprehensive audit trails provide a record of every attempt - successful or denied - by LLMs to access data. The extension of these security controls to MCP allows engineering teams to develop technology that incorporates AI without opening new avenues for unauthorised access to company data. By supporting both machine and user-driven LLM workflows, Teleport states its platform is positioned to accommodate a range of AI integration scenarios while maintaining a strong security posture. Follow us on: Share on:
TECHx
3 days ago
- Business
- TECHx
Qualys Unveil Agentic AI for Real-Time Cyber Risk Management
Home » Tech Value Chain » Global Brands » Qualys Unveils Agentic AI for Real-Time Cyber Risk Management Qualys, Inc. (NASDAQ: QLYS), a provider of cloud-based IT, security, and compliance solutions, has announced new Agentic AI capabilities on the Qualys platform. The new AI fabric powers a marketplace of Cyber Risk AI Agents. These agents deliver real-time insights across all attack surfaces, prioritized by business impact. They also help reduce risk and operational costs through autonomous remediation at speed and scale. This enables a more efficient and intelligent Risk Operations Center (ROC). As cyber threats grow in volume and complexity, security teams face millions of exposures with little context. Manual processes lead to delays and unaddressed vulnerabilities. To solve this, Qualys introduced Agentic AI to eliminate repetitive tasks and enable risk-focused workflows. According to Tyler Shields, principal analyst at Enterprise Strategy Group (ESG), 'Integrating Agentic AI into the Qualys platform marks a major leap from reactive response to real-time risk reduction.' He added that this innovation supports faster remediation and greater accuracy. By embedding Agentic AI into Enterprise TruRisk Management (ETM), Qualys enhances risk-centric automation. ETM already aggregates exposures to align cyber risk with business value. With the new AI fabric, Qualys now offers pre-built AI agents for threat prioritization and remediation tailored to each organization. The Cyber Risk Assistant is also introduced. This prompt-driven tool helps teams navigate risks, translate exposures, and deliver context-aware insights through autonomous operations. The Qualys Marketplace now features: Continuous risk insights from fragmented exposures, using pre-built AI agents. from fragmented exposures, using pre-built AI agents. Adaptive remediation via AI agents like the Microsoft Patch Tuesday Lifecycle Agent. via AI agents like the Microsoft Patch Tuesday Lifecycle Agent. Custom AI agents through a no-code interface, enabling reusable, automated workflows. 'Qualys Agentic AI, embedded into Enterprise TruRisk Management, is transforming how organizations manage cyber risk,' said Sumedh Thakar, President and CEO of Qualys. He emphasized that CISOs can now augment their teams with intelligent AI agents for faster, strategic risk reduction. This launch represents a step forward in autonomous cybersecurity and smarter operations powered by AI.
Channel Post MEA
3 days ago
- Business
- Channel Post MEA
Qualys Unveils Agentic AI-Powered Risk Operations Center
Qualys has unveiled several new Agentic AI capabilities on the Qualys platform. The new AI fabric introduces a marketplace of Cyber Risk AI Agents delivering real-time risk insights across all attack surfaces, prioritized by business impact. Additionally, it reduces risk and operational costs by autonomously remediating with speed, scale, and accuracy, all while powering a smarter, more efficient Risk Operations Center (ROC). Amid a surge in the volume and sophistication of cyber threats, amplified by the growing complexity of an ever-evolving attack surface, teams are grappling with millions of exposures while lacking the context to map them against business priorities. Without self-orchestrating AI agents to turn data into insights, and prioritize and remediate risks in real time, security teams face manual bottlenecks and lingering exposures. Qualys addresses this with Agentic AI—eliminating repetitive tasks and enabling autonomous, risk-focused workflows that empower teams and accelerate protection. 'Cybersecurity has never been able to keep pace with the volume of enterprise exposures due to human-scale prioritization and remediation,' said Tyler Shields, principal analyst at Enterprise Strategy Group (ESG). 'Integrating Agentic AI into the Qualys platform marks a major leap—from reactive response to real-time risk reduction. With autonomous remediation and intelligent prioritization, this type of innovation enables faster risk reduction, more efficient resource usage, and greater accuracy in recommended actions. This evolution shifts security teams from tactical responders to strategic agentic AI orchestrators, bringing us closer to a future of self-healing cybersecurity.' By embedding Agentic AI into Enterprise TruRisk Management (ETM), Qualys enhances its risk-centric automation capabilities—delivering faster, more intelligent decision-making. Already a leading cornerstone of the ROC, ETM aggregates exposures to measure, communicate, and eliminate cyber risk aligned to business value. Now, with the new AI fabric, Qualys delivers pre-built AI agents that automate threat prioritization and drive remediation strategies tailored to each organization's risk appetite and environment. It also introduces the Cyber Risk Assistant—a prompt-driven interface that helps teams navigate the risk journey, translate millions of exposures, and deliver context-aware risk insights with autonomous operations. The Qualys Marketplace of ready-to-use AI agents delivers: Continuous Risk Insights and Prioritization from Fragmented Exposures – Pre-built AI agents autonomously and adaptively drive every step of the cyber risk journey from continuously discovering your external attack surface with a hacker's-eye view, to proactively assessing risk against trending industry threats, and prioritizing those risks based on the context of your unique assets and environment. Thus, helping organizations reduce the cost and complexity of risk operations. Adaptive Remediation for the Highest Security Posture – With attackers exploiting vulnerabilities in under 18 days, cybersecurity and IT teams are focused on reducing mean time to remediation (MTTR). Adaptive Risk Remediation AI Agents like the Microsoft Patch Tuesday Lifecycle Agent continuously triangulate prioritized vulnerabilities, correlated remediation techniques, and asset context to drive faster, more transparent risk remediation. This reduces cost and time to close vulnerabilities. Build Your Own AI Agent – Security teams can create custom, no-code, pretrained AI agents tailored to their specific business needs. These agents can be trained to perform specialized tasks autonomously and reused as needed—enabling scalable, repeatable automation for risk management workflows unique to each organization. 'Qualys Agentic AI, embedded into Enterprise TruRisk Management is transforming how organizations manage cyber risk and powering a smarter, more agile Risk Operations Center,' said Sumedh Thakar, president and CEO of Qualys. 'It's ushering in a new era where CISOs can augment their security teams with intelligent AI agents that perform autonomous analysis and take decisive, high-impact actions to reduce risk faster, more strategically, and with greater efficiency.'
Forbes
15-07-2025
- Business
- Forbes
It's Time To Put AI Agents To Work
AI agents offer big productivity boosts for nimble organizations seeking a competitive advantage. This playbook can help you get started. Few emerging technologies will afford organizations more opportunities to accelerate productivity and transform business operations than agentic AI, whose promise is surpassing even that of its generative AI (GenAI) cousin. Whereas GenAI tools are bound by their programming logic, agents will be given goals and entrusted to complete them, using contextual understanding and available data. Agents will perceive and learn from their environment and reason or 'think' their way through to accomplish the objectives assigned to them. In service of these goals, agents will also work with fellow agents, as well as other applications, and learn from each digital interaction to improve the way they operate. Excitement around agents' ability to autonomously execute business operations is growing. Eighty percent of organizations cite AI agents as the top or a high priority compared to other AI initiatives, with 51% already actively deploying agents, according to research from Enterprise Strategy Group.1 When strategically deployed, agentic AI will amplify your workforce's productivity and pave the way for better business outcomes. As with many new technologies, agentic AI will bring change management challenges and you may need help navigating this new frontier. That's why Dell and NVIDIA have created this eBook, which helps leaders both imagine the art of the possible and approach agentic AI use cases intentionally. How Agents Work For You Common use cases are emerging to illustrate agents' potential to augment, or even transform, organizations' operations. Customer Service. Organizations have long leaned on chatbots to field customer inquiries. While chatbots answered inquiries based on pre-programmed logic, agents have far more agency in addressing concerns, including wider latitude to solve problems in accordance with best-in-class service goals. For example, agents can use available data to personalize interactions in natural ways or deftly escalate interactions to a human if needed. Also, by integrating with modern CRM systems, agents can resolve inquiries, reducing wait times and improving customer satisfaction. Supply chain. Organizations have long paired predictive AI with human estimation to forecast demand for their products with middling success. Agents can markedly improve upon this process, monitoring and managing inventory systems 24-7. In case of inventory shortfalls, they can reroute shipments, adjust schedules and improve from every transaction over time. Software Development. A lot of code generation can be tedious and repetitive; agents can remedy that problem. A software programming agent might create new application features, check-in code, merge changes, review code on the fly and eliminate bugs. Digital twins. As great as their potential is, digital twins tend to be static representations of physical environments, such as machines. Analyzing data from sensors and edge devices, agents can simulate operations within digital twins, monitor performance, and anticipate failures. Most importantly: teams of agents can triage problems and help remediate them. These represent just a smattering of agentic AI use cases. View more in this Dell-NVIDIA eBook. Start Cautiously, But Start Now Now for some caveats. Watch a video of an AI agent in action and it feels like you're witnessing digital wizardry. As exciting as these early offerings may be, additional steps are required to facilitate broad agentic AI adoption. A lack of interoperability and standards persists, which hinders agents' ability to work together across organizations or collaborate across software platforms. When these hurdles are cleared there will be few limits to what agents can do. Now is the time to explore agentic AI and its impact on your IT architecture and platforms. Follow best practices and prioritize specific use cases that will most benefit your organization and drive meaningful return-on-investment. Then carefully plan how to integrate agents into organizational workflows across enterprise software systems and other IT operations, as well as physical environments such as operations systems and edge devices. Codify an agentic operating model and a strong governance plan. Lean on existing agentic AI success stories and begin building and piloting so that your agents can get their digital hands–on keys. Only by exploring the art of the possible can you steer your organization through the learning curves and move the needle from aspiration to accomplishment. Agentic AI requires powerful systems comprising modern and flexible infrastructure. The Dell AI Factory with NVIDIA, a framework that incorporates services, AI software and infrastructure, provides the foundation to power agentic AI systems at scale. NVIDIA NeMO and NIM microservices provides developers the tools they need to build and fuel agents. Partnering with Dell and NVIDIA, you can confidently implement agentic AI to unlock new levels of efficiency, automation and productivity. When it comes to agents, you're only limited by your organization's imagination—and appetite for innovation. What will your organization build? Learn more about the Dell AI Factory with NVIDIA.
Techday NZ
25-06-2025
- Business
- Techday NZ
Datadobi unveils StorageMAP 7.3 with enhanced automation tools
Datadobi has announced the release of StorageMAP 7.3, introducing new features aimed at improving automation, governance, and compliance for organisations managing unstructured data across diverse storage systems. Policy-driven workflows The updated version of StorageMAP now includes policy-driven workflows, allowing administrators to set up automated tasks that are initiated by specific triggers, such as scheduled times. These workflows can be previewed with a "dry run" capability to ensure accuracy prior to execution. According to Datadobi, this enhancement supports use cases such as the automated archiving of data, establishing pipelines for feeding GenAI applications, and isolating non-business-related data into designated quarantine areas. After policies are configured and activated, StorageMAP executes the workflows automatically as scheduled, reducing the need for ongoing manual oversight. Precision deletion StorageMAP 7.3 has expanded its data management toolkit with the introduction of granular file-level delete functionality. This feature enables administrators to pinpoint files based on specific criteria and use them as input for targeted deletion tasks. Following execution, each deletion is detailed in a report documenting the scope and outcome of the job. The company states that this development is particularly relevant for scenarios where bulk deletions are impractical due to mixed content within directories. By providing a finer level of control, administrators can develop and deploy more precise deletion strategies. Compliance-focused object mobility Object migration capabilities have also been enhanced in StorageMAP 7.3, with support added for moving locked objects between S3-compatible platforms. This change allows organisations to transfer compliant data stored in Write Once Read Many (WORM) formats to alternative storage vendors without losing retention dates or legal holds. During migration or replication tasks, administrators now have the option to designate the storage class for S3 objects, enabling data to be allocated to the appropriate tier immediately and avoiding the need for subsequent lifecycle management policies. "StorageMAP 7.3 marks a major step forward in enabling true automation for unstructured data management. Organizations can now define and execute policy-based actions at scale, removing the bottlenecks inherent to existing manual processes, making their file and object storage environments far more responsive to operational needs," said Carl D'Halluin, CTO, Datadobi. Simon Robinson, Principal Analyst at Enterprise Strategy Group, now part of Omdia, commented on the update: "Unstructured data continues to proliferate across hybrid environments and organizations need solutions that not only provide visibility but also enable decisive action. Datadobi is addressing several operational pain points by introducing policy-driven automation and greater control over how data is managed, deleted or migrated, especially in compliance-sensitive scenarios." Operational impact As data landscapes grow increasingly complex, enterprises are seeking solutions that offer greater control while limiting the operational burden. StorageMAP 7.3 is designed to address these challenges by reducing the manual effort required for day-to-day data management and helping organisations move sensitive or business-critical information without jeopardising compliance or system performance. By extending workflow automation, providing more targeted control over data deletion, and improving compliance in object migrations, StorageMAP 7.3 addresses key concerns for administrators and data teams working across heterogeneous storage environments. Follow us on: Share on:
