Artificial intelligence (AI) has the potential to completely transform cyber security. But this isn’t news to any security professional. In recent years, the hype surrounding AI and machine learning has grown exponentially. And now, it can be difficult to separate hype from reality.
Making AI practical for your cyber security operations requires a focus on data intelligence. Artificial intelligence and machine learning go far beyond the limits of human capacity to process massive amounts of data generated by your security stack. When applied to threat intelligence, these technologies give you 24/7 monitoring that keeps you ahead of increasingly sophisticated attackers.
But what exactly does AI threat intelligence look like? Taking advantage of this technology requires a deep understanding of the role of AI in cyber security and how it impacts threat intelligence.
The Double-Edged Sword of AI Threat Intelligence
Threat intelligence provides context and action-oriented advice about cyber threats that target your business. Introducing AI to this information serves to deepen the insights and ensure scalability as attack volumes increase.
However, AI in cyber security is a double-edged sword. In a report questioning whether artificial intelligence poses a threat, Malwarebytes says that “Because AI-enabled malware would be better equipped to familiarize itself with its environment before it strikes, we could expect harder-to-detect malware, more precisely-targeted threats, more convincing phishing, more destructive, network-wide malware infections, more effective methods of propagation, more convincing fake news and clickbait, and more cross-platform malware.”
This is where AI is taking the threat landscape. Without taking advantage of AI threat intelligence, you risk falling behind and experiencing more attacks and breaches. That’s why cyber security leaders are investing in AI. According to a Centrify survey:
- 69% of enterprises see AI as a necessary investment for responding to cyber attacks
- 73% of enterprises are already testing use cases for AI in cyber security
- 51% of executives see cyber threat detection as the primary use case for AI
- 64% of security leaders believe AI lowers the cost to detect and respond to breaches
Already, 61% of enterprises say they can’t detect breach attempts without AI systems. And with 48% saying they plan to increase AI cyber security budgets by an average of 29% in 2020, it’s important to find the most effective ways to implement this technology.
When your human analysts and AI threat intelligence systems work together, you can achieve proactive cyber security that stays ahead of the latest attack vectors.
Balancing the Advantages and Disadvantages of AI Threat Intelligence
The biggest mistake security professionals can make is thinking that AI-enabled protection devices will automatically protect against all cyber security threats. While AI systems can carry out certain human tasks and provide firsthand protection in some cases, they can’t fully replace your human analysts.
Rather, the most effective way to implement this technology is to strike a balance between AI systems and your human security analysts. The right combination of AI and expert experience will ensure that your algorithms are continuously aloptimized to protect against threats that exist today and the ones that will emerge tomorrow.
This is why AI and cyber threat intelligence pair so well together. Threat intelligence can be infused with the best aspects of AI, including:
- Data Processing: There’s more activity happening across your network than human analysts can keep track of. All the data generated by security and monitoring tools can be fed through AI-enabled systems to fuel a threat intelligence program. Even if threat detection can’t be entirely automated, AI can distill key information about anomalies for human analysts to review.
- Machine Learning: AI-enabled systems can introduce machine learning to your security operations. That means that your threat intelligence program can evolve over time to identify attacks more effectively. When machine learning works with threat intelligence, your analysts will be able to work from a more accurately prioritized list of risks and address them accordingly.
- Contextual Analysis: AI ensures your threat intelligence system isn’t relying solely on first-party data. The data collected by your networking tools is critical. But your threat intelligence program must also address unknown risks. Artificial intelligence can analyze data from all kinds of third-party sources to keep you ahead of the thousands of new threats that emerge each year.
We haven’t reached a point that AI systems can predict, prevent, detect, and respond to cyber attacks with 100% accuracy. That may come in time. But for now, you’ll find more success applying AI to threat intelligence so you can stay ahead of constantly evolving attacks. With human analysts making changes to AI algorithms and applying their expertise to prioritize risks, you’ll be able to take a more proactive approach to cyber security overall.
But not all teams have the resources to build out sophisticated threat intelligence practices—with or without AI. This is where managed threat intelligence can help. Working with the right partner will ensure you can actively monitor multiple threat sources across the clear, dark, and deep web to gain visibility of targeted threats to your organization’s specific assets, data, and people. A managed threat intelligence provider can use AI algorithms to rapidly analyze adversary trends, social engineering attacks, IOC sharing sources, and all the alerts generated by your security and monitoring tools.
Whether you’re just starting to take advantage of AI or you’re in the earliest days of threat intelligence, the best thing you can do is stay ahead of the latest news and technology trends. That’s why we built the Cyber Hub for security professionals.
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