Mark Lynd
Asking cybersecurity question in chatGPT
Are you keeping pace with the relentless evolution of cybersecurity? Have you integrated the most recent technologies into your security infrastructure to tackle the ceaselessly emerging threats, or are you clinging to the comfort of time-tested yet increasingly obsolete methods? Unless you are part of the small fraction of businesses that have already embraced generative AI for their cybersecurity needs, it's time to seriously contemplate its adoption.
Many security manufacturers are racing to include generative AI capabilities in their offerings. Microsoft was one of the first with their Security CoPilot. Other manufacturers are doing the same, so expect to see a bunch of new announcements soon.
In this article, we will take a look at both the pros and cons on utilizing generative AI to improve your cybersecurity posture.
What's The Fuss About Generative AI?
It's a subfield of artificial intelligence that's focused on creating new, previously non-existent data, spanning across various formats such as text, images, or even intricate code. This isn't merely a shiny new toy in the tech landscape—it's a revolution for cybersecurity. The reason? It can recognize and respond to novel, sophisticated forms of attacks that would have likely eluded traditional systems, ensuring your organization stays one step ahead.
How Generative AI Can Improve Cybersecurity
Let's delve deeper into the workings of generative AI in the cybersecurity realm:
- Unearthing Hidden Threats: Generative AI is like a detective with a magnifying glass, meticulously sifting through mountains of data, unearthing patterns that scream 'threat.' By doing so, it can spot nascent forms of attacks, offering a preemptive strike capability.
- Swift and Effective Response: Detection is only the first step. Generative AI excels in response too. It can react to attacks faster and more effectively than traditional systems, nimbly producing custom armor for unseen malware, ensuring your digital assets remain unscathed.
- A Trustworthy Compliance Ally: Generative AI can prove to be an invaluable aid in the ever-so-tricky realm of data protection and privacy regulations. It can generate detailed, timely reports on data breaches and other security incidents, turning the tedious task of compliance into a walk in the park.
You could be wondering why you should consider generative AI in your cybersecurity strategy at all. Here's some reasons why:
- Boost Detection Rates: Generative AI can uncover new forms of attacks that conventional systems could easily overlook. It's akin to having a vigilant security guard with night vision goggles, ready to spot danger in the darkest corners.
- Slash Response Time: Generative AI is the sprinter of cybersecurity. It can respond to attacks in record time, much quicker than conventional cybersecurity systems, providing an immediate and robust shield against intrusive malware.
- Significant Cost Reduction: Generative AI takes over a host of tasks related to threat detection and response, effectively slashing the cost of cybersecurity and freeing up resources for other essential business operations.
- Enhance Compliance Capabilities: Generative AI shines as a compliance partner. It can significantly aid in meeting data protection and privacy regulations by automating the generation of comprehensive, compliant reports.
However, the advantages of generative AI extend far beyond the higher-level more strategic ones listed above. Here are some additional ways it can bolster your cybersecurity:
- Craft hyper-realistic phishing emails: Generative AI can create convincingly realistic phishing emails that are more likely to deceive users. By doing so, it can help organizations raise awareness about the intricacies of phishing attacks, significantly reducing the risk of data breaches.
- Create bespoke security policies: Generative AI can craft security policies customized to the specific needs and risk profiles of an organization. This tailored approach can significantly enhance your security posture, reducing the risk of potentially crippling cyberattacks.
- Thoroughly test security controls: Generative AI can comprehensively test security controls, spotting vulnerabilities that could be exploited by malicious entities. By thoroughly testing security controls, it allows organizations to mend these weak spots, fortifying their defenses against cyber threats.
Early Entries in to the Security Race
Here is a list of security manufacturers that have announced generative AI in their offerings (there are others):
- Microsoft has announced Security Copilot, a tool that uses generative AI to help security analysts find and fix security vulnerabilities.
- SentinelOne has announced a new threat hunting platform that uses generative AI to identify and respond to threats in real time.
- Skyhawk Security has added a threat detection feature to its cloud threat detection and response platform that uses generative AI to identify and respond to threats in real time.
- Tenable Research has released four tools on GitHub that use generative AI to identify vulnerabilities faster and more efficiently.
- Overhaul has announced RiskGPT, a feature for its compliance and risk platform that uses generative AI to improve supply chain visibility, incident response time, and risk assessment capabilities.
- Palo Alto Networks has announced Cortex XDR, a next-generation security platform that uses generative AI to identify and respond to threats across the entire enterprise.
- Fortinet has announced FortiAI, a suite of security products that use generative AI to improve threat detection, response, and prevention.
- Cisco has announced SecureX, a security platform that uses generative AI to automate security operations and improve threat response.
- Check Point has announced Infinity Next, a security platform that uses generative AI to identify and respond to threats in real time.
The manufacturers listed above are just a few of the security manufacturers that have announced generative AI in their offerings. As generative AI technology continues to develop and more uses cases and data are unveiled, we can expect to see even more security vendors adopt this technology to improve their offerings.
There Are Challenges to Using Generative AI
On the other hand, deploying generative AI in cybersecurity isn't without it's concerns. There are some hurdles that organizations should be aware of:
- Data Needs: Generative AI is like a knowledge sponge—it requires a large volume of data to train and fine-tune its accuracy. Organizations that do not have access to such extensive data may find this a significant challenge.
- Bias: Generative AI can exhibit bias if it is trained on an unrepresentative dataset, leading to false positives or negatives. This can significantly undermine its effectiveness and reliability. It is important to recognize and try to eliminate or dramatically reduce any bias in the data and results.
- Complexity: Generative AI models can be intricate and difficult to understand, making it a challenge for organizations to utilize them effectively. Focus on having good training data and a strong testing program.
Wrapping Up
Generative AI is a burgeoning technology poised to transform the cybersecurity industry with security manufacturers leading the way. While generative AI does have a few challenges, the potential benefits likely outweigh them depending on your proposed use. Just seeing all the investments and announcements by the security manufacturers lends credence to the potential for generative AI in cybersecurity.
As the cyber threat landscape continues to evolve rapidly, generative AI is expected to play an increasingly pivotal role. Especially in improving the security posture of organizations against increasingly more sophisticated and prevalent cyber threats. So, it is time to stop watching from the sidelines and look at adding generative AI to your security tool chest.