The Network Is Dead, Long Live The Application Network!
CIOREVIEW >> DevOps >> NEWS

The Network Is Dead, Long Live The Application Network!

CIO Review

Protecting applications is getting more complicated and complex. Applications must attach to networks exposing them to all the insecurities that come with it. What if we could stop all attacks that start with scan and exploit and make the traditional network security[1] entirely irrelevant? Threats could exist on the network and not attack our applications. What if it was easy, free and open source?

Security must be easy to adopt, run, maintain.

Before we answer the question of making traditional network security irrelevant as a standard, let’s position the problem:

◙  We all care about security – but man, it’s hard. It’s so hard that we don’t have the time to spend on it. We end up focusing our time on implementing features – NOT security.

◙ All networks are insecure. Period. The purpose of a network is transmitting, exchanging or sharing data and resources – not security.

◙ Insecure networks have us being crushed in the cybersecurity war. It‘s too cheap and easy for malicious actors to launch attacks, laterally move and exploit. We implement elaborate, time-consuming and costly controls and infrastructure to protect our applications, and still, malicious actors make massive revenue causing enormous costs for society.

• System operators must be ever vigilant to stop vulnerabilities being exploited by malicious actors across the network – watching email lists, scanning for updates, coordinating change windows and downtime, implementing patches.

• The zero trust security model was created to reduce network risks by leveraging strong identities and the idea of “never trust, always verify” but it is historically hard to implement and put the onus on the application consumers, not application creators.

Security is hard. But it is mandatory. Security must be easy to adopt, run, maintain. When it is, it becomes standard to the benefit of everyone.

Let’s demonstrate this using a case study. A little over ten years ago, when using a browser to access websites, all data was transferred using the unencrypted HTTP. Then (free and open source) technologies like HTTPS Everywhere and Let’s Encrypt came along. HTTPS was a great idea and became so easily accessible and vastly available that ALL major browsers implemented it, leading to the retirement of HTTPS Everywhere.

We need to go through the same process to secure our applications. Securing the network, which is impossible, must become a thing of the past, just like HTTPS Everywhere.

Foundational truths about networks

The best way to protect our applications is to make security so easy and free that it becomes a standard that everyone can implement. The network as we know it is no longer sufficient. We need to reinvent it.

Luckily, we have the core technology concepts to deliver this. We need to use first principles thinking to dig deeper until we are left with only the foundational truths of a situation.

• Zero trust security model: This provides principles including strong identity, authentication and authorization, account-based access control policies, etc.

• Network virtualization: This allows us to create overlays virtual networks independent of the underlying transport networks.

The foundational truth is that networks are built to transmit, exchange, and share data. While zero trust and virtualization can be applied to networks, we are bolting on solutions that do not fully solve the problem. We need easy and secure, not complex and bolted on. It is only by recognizing that just because “we’ve always done it this way” does not mean we always have to; we can reinvent the network.

Reinvent the network by eliminating the network

The only way to square the circle is to embed zero trust, programmable networking into our applications based on open source technologies that are easy and free. This reinvents the network by putting it inside the application. As Bruce Lee said, “be water, my friend. Put zero trust networking inside the app and it becomes the app, run your application on the internet and it becomes the internet. Application connectivity is secure by default while isolating apps from the internet, local, and host OS networks. App communication cannot occur until explicitly authenticated andauthorized based on a strong embedded identity. This isolation from the underlay, including no exposed/listening ports, stops malicious external actors from exploiting the network. These attacks include zero-day/CVE exploit, DDoS, port scanning, credential/password stuffing, phishing, etc. We have made traditional network security irrelevant.

Free and open source application embedded networking does not just have profound security advantages and the ability for us to focus on value-added services and features instead of hard security; it also helps us to reduce business costs and vendor lock-in. These applications only require commodity outbound internet and eliminate the need for public DNS, VPNs, bastions[2], complex firewall rules, inbound ports, or other proprietary tools and infrastructure. We can programmatically manage the overlay and policies using DevOps tools and methodology without requiring networking engineering skills.

NetFoundry created OpenZiti to provide an open source, free and easy way for the world to embed zero trust, programmable networking into anything and everything. Embedding every application in the world with zero trust will take time – just like securing browsers took time and VPNs were the past! This is why while we keep app-embedded as our north star, we can help you get there by providing applications for all major desktop/mobile operating systems which we call tunnelers. You can use these local programs to protect your existing applications and infrastructure and allow your brownfield solutions to participate in the new, identity-driven zero trust overlay network. Existing applications implement zero trust of the local and internet networks providing an immediate and huge reduction in attack surface. Accessing your apps exclusively over the zero trust overlay network raises the bar on attackers by orders of magnitude. Bad actors can no longer attack targets from afar. They need to be local to the machine to launch an attack reducing the return on investment for malicious actors such as ransomware operators. Uniquely, we have built NetFoundry and OpenZiti so that anyone can employ them in any use case, including hybrid/multi-cloudedge and IoT, user access (incl. DevOps or user remote access) or app-embedded [3].

The only question is, do you want to host your OpenZiti overlay network or let NetFoundry host, run and maintain it for you (including free-forever tiers)?

The network is dead, long live the application network.

Try for free – with OpenZiti or NetFoundry

[1] We refer to traditional network security as things such as public DNS, VPNs, MPLS, bastions, APNs, proxies, complex firewall rules, inbound ports or other proprietary tools and infrastructure.

[2] Read about how NetFoundry took our bastions offline here

[3] If you just want to see more Ziggy outfits, check out this blog

More in News

A software engineering and AI analytics purchase can fail long before model accuracy becomes a concern. The bigger challenge is often the handoff between existing infrastructure and new analytics, especially when camera networks and edge devices were never designed to share context. Replacing everything may simplify architecture on paper, but it can strain capital requirements and stretch deployment timelines. Buyers need to know whether a platform can work across existing technology boundaries without turning modernization into a wholesale infrastructure project.  Integration depth is therefore more revealing than the number of AI features on a product sheet. A useful platform should accept heterogeneous inputs and expose their data through a common control layer without forcing every piece of existing hardware to conform to one technical standard. It should also preserve the usefulness of legacy assets while making their information accessible to newer analytics. That matters in distributed environments where hardware replacement may be slow or economically unjustified. The real question is whether modernization can proceed around installed infrastructure rather than requiring a clean slate.  Real-time analytics creates an attention problem of its own. Video feeds and sensor events can overwhelm staff when every detection is treated as equally important. Buyers should examine how a platform separates routine activity from events that merit human review, and then look at how quickly those events reach the people responsible for validation. The difference between raw monitoring and useful analytics lies in this filtering step. A system that produces more alerts without improving prioritization merely transfers workload from observation to triage.  Architecture becomes more consequential as data volumes rise. Sending every high-definition stream to a centralized cloud environment can create avoidable bandwidth costs and response delays. Edge processing can reduce that burden when analytics are performed close to the source and only selected information moves upstream. Yet edge deployment introduces management demands. Devices and application services still need a coherent way to exchange information and support later investigation. Buyers should also examine how much infrastructure complexity is added when analytics move closer to the source.  “ CFBD ’s hybrid architecture connects legacy environments to newer computing infrastructure through hyperconvergence and virtualization.” Searchability deserves equal importance. Once video or sensor data has been transformed into structured metadata, teams should be able to move from live detection to later investigation without manually reviewing hours of footage. Useful systems retain descriptive attributes and behavioral context in forms that can be queried quickly. The buying question is whether that context survives across live monitoring and retrospective analysis rather than being trapped in separate workflows. Fast retrieval matters because delayed investigation can erase much of the advantage gained from real-time detection.  CFBD merits consideration for buyers facing this mix of integration pressure and analytics workload. Its AZOR Ecosystem, a real-time data orchestration platform, centralizes video and sensor information. AZOR Panel, a centralized monitoring interface, receives AI-filtered events for human validation. AZOR Analytics, a video analytics component, supports real-time and forensic analysis, while edge gateways can process video near the source and pass lighter event data upstream. Its hybrid architecture connects legacy environments to newer computing infrastructure through hyperconvergence and virtualization. The fit is strongest where replacement costs and operator overload are material constraints. For organizations that need AI analytics without discarding usable infrastructure, CFBD is a practical choice.  ...Read more
Customer outreach is moving faster than many compliance programs were designed to govern. A campaign assembled in hours can pass through several applications before a call, text, email or prerecorded message reaches a customer, while autonomous agents compress that cycle further. The exposure is no longer limited to whether a record appeared on a suppression list. Consent status, channel permissions, time-of-day rules and state or federal restrictions can change the answer at the moment of contact. A platform that checks too late leaves legal teams reconstructing decisions after the communication has already occurred. Static controls also create a quieter commercial problem. Large enterprises often carry separate customer records across business units, and a broad opt-out can be applied far beyond the product or channel the customer intended. Conservative suppression may reduce legal exposure, yet it can also remove legitimate audiences from campaigns and weaken the return on CRM or marketing technology investments. Effective governance needs enough context to distinguish a prohibited contact from an allowable one without forcing every business unit to maintain its own interpretation of the rules. The harder test is whether those distinctions survive as consent records move between systems and outreach programs change. “Gryphon’s deterministic rules-based decisioning evaluates contact permissions in real time, while automated evidence capture gives legal and compliance teams a defensible record of why each decision was made.” Speed matters at the decision point, not merely during campaign preparation. List scrubbing and periodic audits remain useful for certain tasks, but neither is designed to govern communications that originate across contact centers, individual employees, enterprise applications and autonomous agents. Decisioning should sit inside the existing workflow and evaluate the applicable permissions before outreach proceeds. The answer also needs to return quickly enough that compliance does not become a queue. Enterprise scale is equally important. A control layer that works only for one channel or one application recreates the same fragmentation it was purchased to remove. Policy changes also need to propagate without campaign teams waiting for separate rule updates in each downstream application, especially when restrictions take effect quickly. Defensibility separates governance from simple blocking. Executives should expect a clear record of the rule applied, the evidence used, the policy version and the reason a communication was allowed or stopped. Those records need to remain searchable as regulations and internal policies change. Deterministic decisioning has particular value where an organization must later explain exactly why a contact was permitted. The same discipline helps compliance teams identify oversuppression rather than treating every uncertain record as unusable. Buyers should also examine how readily the platform connects to existing CRM, contact-center, marketing automation and governance systems, since a long replacement project can undermine the speed advantage that real-time controls are meant to provide. Gryphon  is the premier choice for enterprises that need contact governance embedded directly into customer engagement rather than added as a later review. Its platform applies real-time controls across voice, SMS, email and interactions generated by AI agents while integrating with existing enterprise applications. Gryphon’s deterministic rules-based decisioning evaluates contact permissions in real time, while automated evidence capture gives legal and compliance teams a defensible record of why each decision was made. Compliance Hub extends that visibility into audit research and reporting. The platform also identifies contacts suppressed too broadly, helping organizations preserve legitimate reach without relaxing policy enforcement. For buyers balancing regulatory exposure against legitimate customer contact, point-of-contact enforcement paired with documented decision logic makes Gryphon a practical recommendation. ...Read more
Disconnected data work rarely begins at the pipeline itself. The delay often appears earlier, when a proposed data product moves from a business idea into requirements, architecture decisions, access controls and a development environment. Each handoff can introduce another tool or approval path, while product context becomes harder to preserve. By the time engineering begins, teams may already be reconciling mismatched project names, duplicated documentation, fragmented ownership and inconsistent setup across systems. Portfolio-level visibility also matters before engineering starts. A platform that preserves business cases alongside product definitions can help leadership compare proposed work, assign teams and select technology stacks without separating prioritization from the delivery path that eventually executes those decisions. That fragmentation becomes expensive when orchestration is purchased as another isolated layer. Data teams commonly work across cloud infrastructure, code repositories, ticketing systems and specialist data platforms, while product managers and architects need continuity across the same work. Replacing that estate is rarely the practical objective. A stronger platform coordinates existing environments while preserving product identity and approved technology choices throughout delivery. Integration depth matters less as a feature count than as a way to remove repeated setup and cross-tool reconciliation. “Calibo can establish access to selected technology stacks and generate CI/CD pathways for controlled movement between development and production environments.” Self-service also needs boundaries. Provisioning development environments, granting access, creating repositories and triggering infrastructure changes can remove substantial waiting time, but only when those actions follow established controls. The useful distinction is whether routine requests can execute from approved templates and policies rather than pass through manual service tickets. That changes the role of platform and architecture teams. Instead of completing repetitive setup on demand, they can establish guardrails that engineering teams use independently. Traceability becomes harder once a project leaves experimentation and enters controlled delivery. Changes to requirements can alter pipeline work, while release movement creates dependencies across development, test, staging and production. Executives need a clear line from the original business case to the technical work that follows, particularly when multiple data products compete for budget or shared engineering capacity. Visibility into status, resource use, dependencies and release progress helps management identify where work is waiting without rebuilding the picture from separate tools. It can also expose queueing between teams before delayed approvals become late-stage release problems. Release control should be treated as part of orchestration rather than an adjacent DevOps concern. Creating a pipeline is only part of the purchase decision. The harder question is whether code and data products can move through governed environments without custom coordination each time. Automated CI/CD setup, reusable templates, policy-based promotion and dependency visibility can make that movement repeatable. This becomes more important for AI-related data work, where experiments can appear quickly but production use depends on controlled access, governed data movement, documented lineage and consistent release practices. Calibo merits recommendation for enterprises that want data orchestration tied directly to the broader delivery lifecycle. Its Data Fabric Studio supports reusable data pipelines. The wider platform carries product context into the development toolchain while automating environment setup. Calibo can establish access to selected technology stacks and generate CI/CD pathways for controlled movement between development and production environments. Its Release Orchestration capability extends that model into deployment governance and dependency management. This gives data teams a self-service framework that reduces manual handoffs while keeping technical work connected to the product context and enterprise controls that initiated it. ...Read more
As software development becomes more reliant on AI, companies have started to focus more on the process of coding, changing, and attributing. Code attribution systems for AI are becoming common for helping engineering professionals know the source of code, differentiate human contributions from that done by AI, and remain visible in the development environment. Their importance goes beyond that of mere tracking since attribution can affect intellectual property management, security assessments, compliance procedures, and engineering performance. However, the implementation of these platforms poses some problems that organizations need to solve before they can effectively use attribution. How Can Organizations Maintain Accurate Code Attribution? An important issue here is the question of establishing accurate attribution in a complicated process of software development. Nowadays, software development includes many repositories, development environments, libraries, automated systems, and cooperation models. The suggestions generated by the artificial intelligence can be accepted, modified, mixed with the existing code, or completely rewritten by the developer. As a result, it becomes difficult to distinguish what part was done with the help of AI and what was developed independently by humans. Data quality also poses a challenge. Attributing authorship requires the availability of development history, history of code changes, prompts, suggestions, and modification patterns. Poor data quality can lead to erroneous findings, especially where organizations employ different methods for software development or use a different set of tools for development. It is imperative for businesses to come up with data standards for the consistency of findings. Issues related to privacy and intellectual property rights make the scenario even more complicated. The source code may include business logic, proprietary processes, and customer data. Companies that choose to implement the attribution platforms need to pay special attention to the way the development data will be gathered, analyzed, stored, and made available. What Makes AI Attribution Difficult Across Enterprise Development? The other challenge is that of incorporating the attribution process into the current engineering process. Companies typically have heterogeneous technology stacks and development processes. This implies that any attribution solution has to be compatible with the source code repositories, issue tracking platforms, code reviews, security mechanisms and so forth. If not, fragmentation and extra manual processes arise. Interpretation is just as crucial. Metrics related to attribution should not be automatically assumed to reflect developer productivity or the quality of code written. While AI can alter how engineers spend their time, more generated code does not imply a better result. Business context regarding maintainability, reliability, reviews, security, and business needs should also be factored in, along with attribution. Attributing AI code is going to be contingent upon transparency, interoperability, and good governance. Enterprises are going to require established processes for attributing contributions made by AI and also explaining how the information is to be utilized. Software systems capable of creating traceable evidence, easy integration, and clear reporting can enable enterprises to create more trust when it comes to AI-enabled development efforts. Taking a good approach towards these considerations will enable enterprises to get visibility regarding the use of AI in coding without having to risk their intellectual property rights and engineering accountability. ...Read more