The impact of AI on autonomous cybersecurity

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The impact of AI on autonomous cybersecurity

The rise of autonomous vehicles and aircraft seems to have been science fiction for a long time, but today these technologies are increasingly becoming a reality. The discussion about the safety and efficiency of these autonomous systems extends to the world of cybersecurity. Just as autonomous vehicles and aircraft are making the road and air safer and more efficient, artificial intelligence (AI) can also dramatically change the way we manage security.

Many parallels can be drawn between autonomous vehicles and what might be called the Autonomous Security Operations Center (ASOC). While this is still a long way off, this blog dives into the key features that could make the ASOC a reality and what this could mean for accelerating autonomous cybersecurity, based on the levels of autonomous vehicles (levels 0-5).

The transition from autonomous vehicles to autonomous SOCs.

In traditional transportation, it is common to see one driver for one vehicle and one pilot for one aircraft. The same is true in cybersecurity: one analyst manages one investigation or incident, just as a driver manages a vehicle or a pilot manages an aircraft. However, with the rise of AI technology, we can expect a shift where one cybersecurity analyst manages multiple incidents simultaneously, similar to how one pilot can monitor multiple aircraft. The road to full automation in cybersecurity, like autonomous driving, goes through a number of levels:

Levels of autonomy in cybersecurity

  • Level 0: no automation

At level 0, human analysts are fully responsible for all security. This includes identifying, analyzing and responding to threats without any support from automated systems. Basic cybersecurity – such as firewalls and antivirus software – provide some protection but require continuous human monitoring and rule adjustment. This level can lead to problems such as a surplus of separate tools, a shortage of specialized skills and a growing attack surface.

  • Level 1: assistance for analysts

At level 1, we see some automated tools that assist analysts. Technologies such as security orchestration, automation, and response (SOAR) and hyper-automation can automate routine tasks such as patch management and alert prioritization. However, analysts still need to monitor these processes and intervene during exceptional or complex situations. The integration of SOAR tools can improve efficiency with automatic execution of predictable tasks, but human involvement remains key.

  • Level 2: partial automation

Level 2 represents a further step in automation. Here, security systems can perform multiple tasks automatically, such as correlating alerts and gathering contextual information. AI systems can recommend responses and even handle some incidents automatically based on predefined criteria. Analysts set rules and workflows and monitor the system’s actions. Although there are great advantages in efficiency and speed, human control remains necessary to ensure proper handling of non-standard situations.

  • Level 3: conditional automation

At Level 3, systems can perform many of the security functions independently under specific conditions. AI-driven platforms are able to analyze and respond to threats based on historical data and pre-trained models. This level of automation provides the ability for the system to function completely independently for routine tasks, but complex or unknown threats can still be relayed to human analysts via a request to intervene (RTI). This hybrid approach provides a balance between automation and human involvement for improved efficiency without compromising the ability to intervene in unknown situations.

  • Level 4: high automation

Level 4 is a step further, where systems can handle complete threat management – including detection, analysis, response and recovery – completely autonomously. These systems work best in environments where the different types of threats are well known and clearly defined. Although the systems work primarily independently, there remains room for human intervention – if necessary. This level of automation significantly reduces the need for constant human involvement, but analysts can intervene manually – in complex situations – as needed.

  • Level 5: full automation

Level 5 represents the ultimate form of automation. Here the system can perform all aspects of security management – from threat management to response and recovery – completely independently, without any human input. The system uses the latest AI technologies and techniques, including quantum computing, to perform complex security analysis and threat modeling. This level provides a fully autonomous approach where the system can respond to and learn from new threats in real-time, take active action to prevent damage and recover from incidents without human intervention.

Conclusion

Advances in AI offer promising opportunities for the future of cybersecurity. AI can increase speed and efficiency, as well as the amount of data that can be processed and analyzed. But even with the most advanced AI systems, the right level of autonomy will depend on an organization’s specific needs and goals. It is important for organizations to properly evaluate what level of automation best suits their situation and goals.

AI has the potential to transform the way we manage security by introducing autonomous systems that operate quickly, efficiently and effectively. As we continue to develop autonomous cybersecurity systems, the benefits of AI will become increasingly apparent. In the future, cybersecurity analysts can focus less on routine operational tasks and more on strategic planning and research. This not only improves efficiency, but also contributes to a sound and more responsive security infrastructure. But while AI offers significant benefits, human expertise remains necessary for strategic decision-making and managing exceptional situations.

This article is offered to you by SentinelOne.

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