What is our primary use case?
We use Vectra AI for endpoints where we are unable to install agents, like endpoint agents, EDR agents, or antivirus tools. For example, BYOD devices or routers in our network. We don't have any control over those, but we need monitoring capability.
Vectra AI can monitor the traffic from the wireless router to the firewall or any outgoing traffic. It can give us an idea of whether there is any C&C or C2 communication or any botnet activity from those source IPs. Without having any agents in the endpoint, it is a network monitoring tool. We use this tool to detect threats within the environment where the assets are unmanaged.
Also, since we tap into certain network points such as firewalls or IDSs, we get more visibility from managed assets as well. So before the endpoint notices the behavior, Vectra notices some of the exfiltration techniques and alerts us.
How has it helped my organization?
Overall, it is good and has reduced our time in identifying the system. It is for unmanaged devices. Previously, if we got an alert from the firewall, it was very difficult to find that particular asset. But with the help of this tool, we can simply run a packet capture and immediately get the hostname and know which user is using it.
It has greatly reduced our time to remediate the situation. We can identify the user, block their account immediately, and sometimes kick that device off the network completely.
It has a confidence level of around 60% to detect insider threats of anomalies, but we mostly need to fine-tune the product. We are still in the fine-tuning process. Even though it has been one year since we implemented the product, the first six months were spent integrating various log servers and determining where to tap.
For the past three months, we have been actively investigating the alerts. When we investigate some of the insider alerts, most of the time it is a false positive because the domain is allowed. Vectra does not know that those are allowed domains, such as OneDrive and SharePoint, to access our network devices.
It considers it malicious because a huge amount of file uploads is seen, according to Vectra. But we know those are known URLs and known behavior. When we slowly started whitelisting, the threat confidence level increased. So right now, for insider threats, it gives around 60% confidence, but around 80% of the incidents were false positives because we are still in the fine-tuning process.
What is most valuable?
The packet capturing feature is very useful, and as the name suggests, AI uses models to detect abnormal behavior. Some of the patent-matching algorithms they use are very advanced and detect threats at a very early stage.
For me, detections from unmanaged networks are one of the greatest values. You can identify threats from BYOD or even mobile devices, which were not handled before.
What needs improvement?
The detection algorithms can be improved at the sensor level rather than doing all the things at the brain. For example, if the sensor has some directional algorithm or detects repeating traffic, it can drop those packets at the beginning itself. There is no need to send that traffic to the brain in order to reduce the bandwidth.
AI is picking up a lot now. There is no manual intervention needed. Whenever a detection happens, it can automatically summarize and give it to you. But Vectra doesn't have those kinds of capabilities. It still needs manual intervention to analyze, and they don't have a summarized kind of output. So that can be improved. But apart from that, the detection models and all the other categories have good support for that.
In future releases, I would like to see Vectra AI to generate a summary of the instance.
For how long have I used the solution?
I have been using it for a year.
What do I think about the stability of the solution?
What do I think about the scalability of the solution?
I would rate it at eight. The remaining two points I'm not giving because it's a fairly new product. So far, it is good as per our test and it is able to scale as well.
The only limit is you need to increase the sensors when you have more traffic. For example, the current sensors can handle up to 50 GBPS of traffic per second. If you need more traffic to be utilized, then you need to buy additional sensors to handle the traffic.
From a technical perspective, there is not much more possible, because there are some hard limits in the hardware. You cannot increase the bandwidth. They have other options to increase with more sensors, but it ultimately ends up being a cost factor.
If you have more money, you can buy more sensors and do it.
In our organization, we are an MSSP provider. We use Vectra, and our entire SOC team, which is around 20 people, uses Vectra for our MSSP. We have two customers who are also using this product. Two of the largest telecom industries in Thailand are using this product to understand their behavior as of now. The approximate number of users in those categories will be around ten.
How are customer service and support?
The customer service and support are good. So far, we have not faced any issues at all.
How was the initial setup?
The setup is a very straightforward process. You need to tap the network traffic at your desired point, and it has two components: a sensor and a brain. The sensor collects the logs and forwards them to the brain, which does the detection and everything. They offer a virtual appliance that you can run in your environment.
The setup process is usually very simple. It took only two days to set up. But, initially, deciding the location of the sensor and other factors took more time. The threat team at Vectra AI engaged with us effectively, provided all the support, understood our architecture and advised us on placing the sensors.
What's my experience with pricing, setup cost, and licensing?
The licensing is on annual basis.
What other advice do I have?
I would rate it at nine out of ten. The one point I'm reducing is because the model can learn itself. If no one is fine-tuning it, for example, every time we find a huge number of alerts, then only we go and look it up and fine-tune the product.
If no one is acknowledging it or it seems like regular traffic, then the product can understand that behavior and have a feedback mechanism to correct it, mark it as a false positive, or whitelist it.
My recommendation:
Understand your network first, and place the sensors in the correct position to receive all kinds of traffic: THC, PDNS, and all those things. If you place the sensors at the egress traffic, you may not receive some of the packets, and you will not have overall visibility.
So the placement of sensors is very important; you need to understand your network to place them correctly.
Which deployment model are you using for this solution?
Public Cloud