bygcastell10-11-201707:09 AM - edited 10-11-201707:14 AM
Artificial intelligence (AI) has become a buzzword in the federal space, and what once was realized only in sci-fi movies, is now a burgeoning reality in IT processes. In fact, just last year, the White House encouraged federal agencies to explore all of the possibilities AI could offer, and the General Services Administration (GSA) launched programs to enable federal adoption of AI.
Furthermore, a recent Deloitte study found that AI could potentially save government 1.2 billion hours and $41.1 billion annually and increase mission-delivery speed by automating processes. However, before government can take advantage of advancements like AI today, agencies must take a few key steps. One area where Brocade has implemented fundamental changes to make way for AI technology is in the network. Below I will explore how agencies can begin to evolve their network technology in order to leverage AI capabilities in the near future:
When federal agency CIOs discuss the challenges that keep them up at night, there’s no lack of topics to explore. However, there’s one issue that is constantly bubbling to the top. According to Professional Services Council and Grant Thornton’s annual CIO study, cybersecurity is the top concern for federal IT leaders. This is likely to only increase, as 81 percent of CIOs in CIO Magazine’s annual study noted a greater involvement in cybersecurity in the most recent survey than in the past.
While there are new, increasingly advanced cybersecurity solutions constantly introduced, cyber criminals are nimble and have many resources at their disposal. It’s too easy for cyber criminals to stay a step ahead given misaligned incentives. In such an environment, it’s critical that agency approaches to cybersecurity start with a solid baseline that lies within the agency’s network. Just like network performance and reliability, security starts with visibility and automation, and successful efforts cannot exist in silos.
Network visibility can reveal a lot about an agency’s systems, from where the majority of traffic flows originate to the times of most activity. Similarly, network insights are valuable from a security perspective. Just as network visibility can identify when traffic flows require a change in network configuration, they can also point to anomalous traffic patterns that likely indicate a security breach. For example, if an agency typically sees most activity coming from within the United States during normal work hours, an influx of activity from Europe at 2:00 a.m. may be enough to trigger concern.
byWilbur_Smith09-06-201710:23 AM - edited 09-06-201710:25 AM
As I was thinking through a topic for this blog entry, I remembered a conversation with a fellow SE describing their customer’s first demo with our new SLX router: “…yeah, and he thinks Air Traffic Controller is a blast!”
Though I didn’t get it initially, the comment was a joking reference to the underlying Linux OS that SLX-OS is built on. It took some Googling to learn Air Traffic Controller is a TTY Console-based game that has survived as a port into Linux. Still confused? Don’t worry, because the Internet is amazing at stuff like this: https://ttygames.wordpress.com/2013/06/14/air-traffic-controller
So is there really a hidden console game on Brocade’s newest routing platform, the SLX? Not quite, but it is inside the Third Party VM (TPVM) that can be deployed on SLX. What’s the TPVM and why should customers care? Before answering, let’s take a step back.
Imagine two cars are racing. The first is a Ferrari, while the second is a 1999 Ford Taurus. The comparison seems unfair, yet this is one way to view the relationship between today’s government IT environment and IT expectations. The Ferrari represents government employee and citizen expectations for security and reliable data access. The Ford Taurus represents aging government networks that cannot keep pace with a wide variety of emerging security threats. In the current vehicles, it’s an impossible race to win.
However, this scenario doesn’t need to be the case. Machine learning in the network can help detect and negate attacks. Similar to the idea of automatically upgrading the engine in the Ford Taurus, weaving real-time intelligence via machine learning into the network infrastructure can help keep pace with emerging threats. In a world where attacks can occur at any time, the network needs agile defensive and offensive capabilities. With machine learning built into the network, a heightened level of awareness is integrated in to your environment to address zero-day threats as well as other service disruptive anomalies.
While many machine-learning capabilities are still being developed, this is the time for agencies to prepare. Government should take three steps to leverage machine learning for your network within the next few years.