Your business already knows that a 24/7 security operations center (SOC) is critical for safeguarding the availability, confidentiality, and integrity of its data and applications. Your customers are active at all hours of the day, threat actors are active at all hours of the day, and you need to be ready to keep everything secure. However, given the size and scope of connected business, having an effective 24/7 SOC is easier said than done.
After all, to know what is actually happening on the network at all times, your SOC has to take in data from an ever-increasing number of sources: endpoints, applications, servers, network devices, cloud applications and even Internet of Thing (IoT) and operational technologies (OT) devices. It becomes a race against time: you need to make sense of all this log data before attackers get in and get access to your most sensitive information. To win this race, you need to build more maturity in your SOC. That requires knowing the challenges that stand between you and the visibility you need, and bringing in the cyber security automation and data science skills to help you surmount them.
The Stakes of the Data Struggle
The stakes of this race are high. In fact, the global mean time to detect (MTTD) and contain a breach is 287 days, and time is money. The average cost of a data breach, according to the latest IBM Cost of a Data Breach report, is $9.05 million, up 10% from the previous year. These costs include the basics of incident response: detection and escalation, notification costs, and post-breach response. But, they also reach further: disclosure of trade secrets, loss of availability of business resources, and lost business due to reputational damage or lost availability.
How Current SOCs Struggle with Data
The SOC has always needed to detect and respond to threats before a breach happens. This has never been more critical, but it has also never been more challenging. Significant causes of the data struggle include:
Modernization of applications
Modern applications, using technologies like cloud, containers, serverless infrastructure, and composable infrastructure, offer more features and are more scalable than ever. But, they are also more difficult to monitor. They produce log data at cloud scale, and all of that data needs to be collected and processed. These modern applications need a modern approach to security. They need the capacity to collect rich data from both the infrastructure and their security controls, and the ability to correlate and make sense of that data quickly. Traditional SOC structures, without cyber security automation or data science, cannot make sense of so much data so quickly.
Fluid threat environment
As business technology has gotten more fluid, attackers have become equally adaptable. Between cloud, mobile, IoT, and work-from-home, infrastructure is sprawling. In parallel, threat actors are able to launch complex, automated attacks more quickly than ever. They are even beginning to embrace technologies like machine learning and artificial intelligence to refine their attacks. The SOC needs to be able to make sense of the threat landscape just as quickly as the attackers can make sense of their environment.
Increasing security complexity
As the technological environment and the threat landscape get more complex, security needs are also growing in complexity. With so many devices producing data and a number of disparate security tools already in the environment, determining a unified plan that you can confidently act upon becomes difficult.
Addressing these challenges requires building deep security maturity. You have to recruit and hire people with a range of specialized skills in data, security, and threat analysis. Given the skills shortage, that can be a lot to ask, especially since attackers will continue to refine and target their attacks as you struggle to increase your knowledge base.
How Data Science and Cyber Security Automation Increase SOC Maturity
There is more telemetry than ever, so the challenge is to make sense of it. Data science and automation allows you to look at that telemetry from many different angles and narrow it down to true, relevant incidents to investigate. The advantages of data science in the SOC include:
- Better detection and faster response of real threats
- Reducing alert fatigue
- Improving SOC team efficiency
Solving these challenges is critical: 31.9% of SOC analysts ignore alerts due to the barrage of false positives. Reducing false positives and efficiently correlating real incidents is critical to identifying and thwarting attacks before they become breaches.
However, building a data science program to do this isn’t as easy as hiring one person to implement it, or buying a single piece of technology that will do this for you. Effectively identifying real incidents demands a strong underlying foundation. It requires understanding the relevance and detection value of the data sources in the environment, and how they relate to the SOC use cases.
Your team must be able to perform data normalization. It must implement Extract, Transform, Load (ETL) processes to take in the data from the disparate sources around the environment, integrate them into a central data store, and get them ready for analysis and processing. Data science in the SOC also requires a detection engine that can apply AI, Machine Learning, statistical anomaly, threat intelligence, and vulnerability scan correlation, since meaningful analysis of data at the scale of a modern enterprise environment requires multiple approaches.
How SOC-as-a-Service Brings Data and Analytics to the SOC
A SOC program informed by data science and analytics is time-consuming to build from scratch. It requires bringing in a team of data engineers and data scientists. It also necessitates bringing in the right technologies to back up their work, to hold the data, and to perform the analysis.
SOC-as-a-Service is a much more accessible choice for bringing a modern SOC to your company. SOC-as-a-Service already has, at hand, the blend of machine intelligence and human expertise that you need to implement a modern SOC.
The cloud-native, next-gen SIEM platform contains essential SOC capabilities, leverages data science and cyber security automation natively, and streamlines the work of the experts in the SOC. It is backed by a team of experts including data scientists and engineers, security analysts and engineers, and threat researchers and hunters. This full-featured team is ready to accelerate threat detection and response across the entire IT environment: both on-premises and remote, including modern infrastructure like cloud services, containers, serverless technologies, and IoT/OT.
Adopting SOC-as-a-Service does not require your business to purchase new technology or space, or hire any staff. You can get on board with it in the span of weeks to a few months, instead of months to years. And, with the co-managed model, your team gets complete access to all SOC operations and data, including analysis, threat hunting, investigation, and response. You can see everything and you can integrate the expertise you have on staff with the SOC-as-a-Service.
SOC-as-a-Service is an excellent option for building SOC maturity in businesses of all sizes. It means better detection now, as well as smooth and cost-effective scaling as your business grows and changes, without the stress and delays of hiring SOC personnel or building out SOC space.
SOC-as-a-Service is for enterprises of all sizes. Even the largest businesses that already have a SOC can benefit. SOC-as-a-Service can improve the efficiency and effectiveness of an existing SOC, and monitor parts of the IT environment that are difficult for some SOCs to reach, particularly cloud (IaaS, PaaS and SaaS).
A Fortune 1000 client of ours typically generates about 60 billion logs in a month, or 2 billion per day. This data includes security telemetry, application logs, cloud logs, and infrastructure logs. Through data science and cyber security automation, we distill this unmanageable amount of data down to about 800 incidents per month (27 per day) that our analysts then investigate further. From that, we pass about five truly significant threats per day to the client, and then work with them to determine a response. This is the power of data science and automation: going from 2 billion logs per day to five significant events.
No matter the size of your business, SOC-as-a-Service increases your security maturity by increasing threat detection rates and allowing for faster response to real threats.
The Next Steps Toward Maturity
Being able to detect and respond to threats is a key component of security maturity. With the current scale and complexity of your business infrastructure, and the use of cloud, container, and IoT technologies only likely to continue to grow, you need a SOC that is ready to handle that volume and complexity of data. Embracing data science and cyber security automation is key to defending your business.
To learn more about how Cysiv SOC-as-a-Service can bring your security program into the next generation, see it for yourself today.