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AISecOps: Why it's Important in Security Operations| Cymune | Cymune

Demystifying AISecOps and Why it’s Important!

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Nov 23, 2021
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Demystifying AISecOps and Why it’s Important!

We are in a very interesting time where a lot of digital transformation is happening across. We are seeing security events happen left right and center. So how do CIOs & CISOs continue paddling and pushing the digital transformation agenda forward while making sure their organizations are safe? Data at a such a crossroad becomes supremely important.

We are all very aware that this world runs on Data and as humans we cannot ever monitor leave apart safeguard it all. That’s where Artificial Intelligence (AI)- enhanced cyber security comes into the picture; to add essential layers of protection for modern enterprise networks. AI adds efficacy and accuracy at the same time diminishes the impact of the ongoing worker shortage in this field.

What we've seen in the last 20 years is that things have changed drastically. A lot of customers in the recent times have pressed the pedal very, very hard to be on the digital journey. What it means for businesses is that there's more data which means there’s far larger digital estate to deal with. And the sophistication of the attack has also grown by leaps. So, all the odds are currently stacking against security operations. Now what that means for organizations is to be very careful about how they design, how they architect, how they even bring thought leadership in the way they want to manage their assets and securely run business in an environment which is completely protected.

In fact, research firm Technavio expects the AI-based cybersecurity market to grow by $19 billion from 2021 to 2025. They cite the heightened complexity of enterprise networking environments, as it’s become a mix of legacy, on-premises infrastructure, and cloud resources, all of which need to be accessed remotely.

Demystifying AISecOps

Artificial intelligence has long been a sort of a ‘Holy Grail’. The skill to create intelligent programs capable of learning from prior activity and employing insights towards solving difficult problems can modernize nearly every aspect of modern business. And AI for Security Operations is no different.

It is also a well-known truth that the talent war is real in security operations. There is simply too much going on, and not enough skilled resources to effectively address the task at hand. One of the key aspects of AI in security operations is about intelligent automation, which enables your limited resources to drastically accelerate detecting and addressing real threats; saving them from getting lost in the ocean of false positives and potentially disastrous oversights. Thus, when SOC analysts’ join hands with AI in their Security Operations, they benefit in many ways.

AI SecOps will have to be the way to deal with security. In fact, a lot of mature organizations have brought parts of AIOps in their environments. AIOps is naturally an extension of what SIEM did for many, many years. So, you ingest every piece of information, bring them together, start normalizing that information, and then start to take what’s important, from all that data or pieces of information you collected.

Now, AI, on top of this is overarching and it makes sure that you have enriched context to make decisions. So, what SIEM did for you was to get all the information together, normalize it, standardize it, and provide you some level of insights, but it is important from an issue standpoint that enrichment of everything that has reached you happens and happens quick. And that’s exactly what happens with artificial intelligence.

Unless you had enriched context, which was the times before AI SecOps, it was making it difficult for organizations to do effective and quicker decision making. Now that is changing with AI in Security Operations.

“AISecOps ingests telemetry data from every source, normalizes it, and prioritizes important alerts. Issues are enriched with context for better and quicker decision making.”

AISecOps Top Use Cases

If we look for classic examples of AI in cyber security, then spam filtering is one such example of AI cybersecurity application that’s top of mind. Today, we can't imagine a world without spam filters and spam filters are nothing but an artificial intelligence application for cybersecurity world. Now drawing from that context, let’s take the analogy into everything that an organization does in operations, whether it is threat detection, remediation, accelerating investigation or even doing threat hunting. The opportunity for you to introduce AI within SecOps is extremely high, but it, it also, again, is very dependent on the environment you are working with. So, one must sit down and see what is the highest model that we intend to go and train for machine learning, and then apply it for doing remediation, or for threat hunting, or other things. What it means is that it can cut across everything that you do from the time of prevention to detection, to response remediation, and then resilient environments that should follow on. Let’s at this point look at some of the top Use cases for AISecOps

Improved Threat Detection and Investigation

AI gives the investigation workflow a structured approach from threat detection to context gathering, to data augmentation, to relationship construction and finally to prioritization, which greatly reduces the time analysts must spend investigating threats early in the investigation process.

Enriched Research and Intelligence Gathering

AI solutions can enhance security alerts by mapping them to tactics and techniques in the MITRE ATT&CK framework. These deeper understanding then helps analysts understand the specific techniques being used by threat actors and its corresponding stage in the ATT&CK life cycle. With these understandings, analysts can learn to anticipate next steps and determine the most effective way to get ahead of potential adversaries.

Lower Cost of Security Breaches

By improving the overall security posture of an organization, AI also lowers the costs associated with security breaches. Reducing dwell times means attacks are identified and resolved in a shorter amount of time, minimizing the impact of security breaches.

Increased Analyst Productivity and Morale

When security analysts leverage artificial intelligence, it increases analyst productivity and streamlines threat detection and investigation processes, saving a significant amount of analyst time. AI does the leg work for analysts and helps them work smarter by taking over the most time-consuming and cumbersome parts away. This saves a big chunk of time and frees up security analysts to focus on more strategic issues, higher-level alerts, and proactive threat hunting.

In Conclusion

These are very tough times for businesses as sophistication and attacks are taking place everywhere and all the time. Any business can be a target today. Most businesses that have reported, if you look back and spoke to some of the CISOs, they never thought they can ever be a target of attack. Now it's become imperative, it isn’t a choice anymore for CIOs & CISOs to contemplate the right time to get AI into their Security Operations. It is very important that organizations start thinking prevention, they start thinking protection, they start bringing in culture of security, culture of innovation for security.

To keep up with the volume, sophistication, and speed of today’s cyber threats, organizations need security operations that can function at machine speed. By applying artificial intelligence, machine learning, as well as integration and automation, organizations can reduce risk and improve efficiency. AI SecOps could possibly be on the curve coming in at a later stage, especially for businesses that are starting to mature their processes and tools, but it is important that it is somewhere on their journey. And to get to that point, CISOs today will have to start putting a lot of things together.

Originally Published on Locuz Inc blog

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