Enterprise Routing Assurance to offer peace of mind to NetOps teams

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アクションRPGとタワーディフェンスが融合。近接・遠距離・魔法を自由に行き来する英雄として戦い、領地を築き、防衛タワーを建てて、次元の裂け目から押し寄せる数百の魔物を食い止めましょう。

Network engineers tell me that their security colleagues now routinely provide a list of requests and questions to help them better assess and manage risk. Threat trends and SOC pressures help explain why. Mandiant’s M-Trends 2026 Report finds that adversaries remain undetected on networks longer, often by establishing persistence on edge devices (firewalls, routers, switches and access points) that frequently lack telemetry. Bad actors are also performing more phases of the attack lifecycle from both edge and core network devices. Additionally, nearly a quarter of the cybersecurity leaders who responded to SANS Institute’s 2026 SOC Survey cited a lack of enterprise-wide visibility as the top barrier to effective security operations. Organizations already have the tools to address visibility gaps, but they are scattered across teams with different priorities. The network team keeps the network running, which requires deep knowledge of the devices on the network and a focus on availability. The security team detects and prevents breaches, hardens those devices and reports to compliance. We have seen early examples of cross-team collaboration in areas we now consider table stakes, like network segmentation and basic patching. However, broader collaboration is required to paint a complete SecOps picture across today’s complex, modern networks. As networking and security become increasingly intertwined, I recommend that NetOps teams revisit foundational security concepts and familiarize themselves with emerging concerns. A shared vocabulary ensures everyone is aligned when coordinating efforts to strengthen cyber resilience. Here are the top areas where NetOps teams should focus their attention. ## 1. Zero trust (never trust, always verify) Zero trust can mean different things to different people, but the basic premise is that you assume the network is already breached and that a single boundary isn’t sufficient. Just because someone authenticated and was authorized once doesn’t mean they can act unchecked as they move through the network and access applications and systems. Users (human and nonhuman, such as AI agents) must be reauthenticated and reauthorized at every step, especially given modern, compound attacks that amplify the impact of initial breaches through rapid privilege escalation. When you keep your shields up, you’re always validating that the device or system granting access is still trusted, that the user is still authenticated and that the user still has permission to perform specific actions. **** ## 2. Least privilege access This best practice is a highly effective way to contain the blast radius. Essentially, you limit access for users (human and nonhuman) and applications to only what is required to do their jobs. Titles and seniority don’t dictate access; role-based access controls do. Tracking every privileged session enables audit capabilities that provide insights to accelerate investigation and response. Separation of duties is a complementary concept. Users responsible for operational mechanics (e.g., Salesforce users) don’t get access to the infrastructure (e.g., logs and permissions), and vice versa. Infrastructure users (e.g., network engineers) don’t get access to operational mechanics (e.g., sensitive customer data). ## 3. Automated patching The race to patchcritical device flaws has accelerated as AI-driven vulnerability discovery and exploitation intensify. Teams must quickly determine the appropriate device state for their environment and how to achieve it. AI-driven automation can help network engineers prioritize critical flaws and deploy the version that best fits business protocols. The latest vendor version isn’t always the right choice; the goal is to strengthen security while maintaining availability. Testing updates before pushing them into production is essential, yet few organizations have lab environments that replicate every device type, model and version. Testing on a low-risk device is the next best option before broader deployment. In environments with 20,000 devices, even a 0.1% failure rate means a substantial number of devices could experience issues, making rapid rollback essential. ## 4. Vulnerability management Patching and compliance checks are the baseline; vulnerability management proactively reduces the attack surface to protect the business. When security hands off a list of CVEs and requests a compliance report, the NetOps team can already be a step ahead. Automatically checking devices weekly, nightly or in real time against critical parameters to verify compliance helps NetOps teams process requests quickly and even pre-empt them by proactively reporting findings and actions taken. Security teams have the evidence required to respond rapidly to audits, with proof of compliance — whether a patch, workaround, configuration change or upgrade was required. ## 5. AI autonomy The 2026 SANS | GIAC Cybersecurity Workforce Research Report found that AI is affecting cybersecurity team size and role structures in 74% of organizations surveyed. AI is helping security teams move faster. However, governance lags far behind deployment: only 21% have a comprehensive AI security framework in place, and 7% have no AI policy at all. Include AI capabilities in Zero Trust and least-privilege policies, and keep AI tools updated and free of flaws. Best practice for NetOps is to follow three guiding principles: * Require transparency into the data sources driving insights. * Ensure the explainability of recommendations. * Keep humans in the loop because they are ultimately responsible for outcomes. Security teams that need help closing the governance gap can apply these concepts to their environments. Network engineers already have deep visibility into device configurations, vulnerabilities, compliance issues and the network’s operational realities. By adding security context to that expertise, NetOps becomes a valuable resource and closer collaborator with SecOps, helping eliminate visibility gaps, identify risks faster and strengthen cyber resilience.

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The concept of probabilistic automation can be off-putting, even divisive. Consider self-driving cars. They’re part of the progression of vehicle automation that includes parallel parking assist, driver assistance and hands-free driving. However, when it comes to self-driving cars, getting on board requires a huge leap of faith that many people aren’t willing to take. Probabilistic automation is the ability to reason, plan and execute actions autonomously within defined governance boundaries. In other words, this is conditional autonomy. These systems use AI to create nuanced, multi-step automations based on specific content, context and learnings. It isn’t instantaneous, and it shouldn’t be mysterious. User visibility and control make it trustworthy—something self-driving cars lack. Probabilistic automation is a powerful tool for solving complex challenges at scale, such as keeping modern networks up and running. It’s time to demystify it for network infrastructure owners and their teams, who need it to be governed, repeatable and safe in production. No leap of faith required. ## A reasonable evolution Whether keeping lines of communication open, medical equipment online, financial transactions flowing or the lights on, network engineers are responsible for maintaining network resilience. Given the scale and diversity of devices NetOps teams need to manage, and the average cost of downtime reaching $15,000 per minute, the urgency to automate tasks to reduce errors, save time and costs, and strengthen resilience has never been greater. So, many NetOps teams have already embraced deterministic network automation to perform predefined, atomic tasks in a prescriptive way. Backups and updates are two common examples. Think about running an automation to back up a fleet of network devices on your network, or to update the OS version for continuous maintenance. Best practice calls for validating the backup once it is complete or backing up a device before you make a change so that you can roll back if there’s an issue. Those additional steps are important but can be time-consuming to automate and vary by device specifications and network environments. So, teams rely on a library of pre-built, tested automations to ensure they run reliably. For more complex and dynamic activities, AI-driven automation offers the opportunity to take automation to the next level by combining probabilistic reasoning with deterministic execution. Chaining together discrete automations is now a practical option, with AI serving as a trusted assistant to support automation activities. AI draws on a library of previously used automations, identifying the discrete tasks that, when combined, closely match the request, and adapting them to the specific situation. It enriches the data used by referencing specific context and incorporating prior experience, and composes the smaller units of automation into a broader automated process. Vulnerability prioritization and remediation is a timely example, as Mythos and other AI-capable models amplify the noise around software flaws and exploits. Probabilistic automation enables teams to take a pragmatic approach to address the challenge. AI can: * Quickly map which vulnerabilities apply to your specific devices and configurations * Identify vulnerabilities that are being exploited in the wild * Prioritize remediation based on the actual risk to your network * Recommend a remediation or a workaround without introducing new risks * Provide and execute remediation steps ## Details matter Here’s where nuance and governance come in. Every NetOps team has its own standard operating procedures. Additionally, network devices have nuances, network environments differ and the time it takes to complete activities varies. Having a detailed understanding of the mechanics of the activity and the automation helps you determine whether each step in the activity aligns with your internal practices, meets device requirements and is set up for success or could create an issue. Notifications and boundaries put people in control, enabling them to get ahead of issues, investigate and make decisions. Chains give you a clear, digestible way to do this without the need for scripting knowledge. As the expert, you validate each step to ensure it aligns with your practices. You set exception-based alerts and designated checkpoints based on your parameters. You then test each discrete automation in your lab environment before adding it to the chain. Finally, you test the entire chain before implementing it into production. This transparency, auditability and human-in-the-loop process allows you to up-level automation and achieve an outcome you can trust. ## Enjoy the ride Self-driving cars face a myriad of situations that they were never trained to handle. Even worse, when cars are in trouble, they don’t always know to ask for help, and there’s no way for a passenger to intervene and stop the car. Ultimately, humans are accountable for actions taken, not machines, so we must treat AI as an assistant and rely on humans as experts. Combining probabilistic reasoning with deterministic execution helps teams manage complex network environments at scale. It also delivers AI-driven automation that NetOps teams can trust in production. It’s the right next step in the evolution of network automation and allows network engineers to enjoy the ride.