Cloud-based AI: What does it mean for cybersecurity?

Cloud computing, AI, and cybersecurity are the three hottest fields in technology. But what happens when you combine all of these together? These fields are increasingly intersecting in 2024, creating opportunities for security professionals to empower themselves with Cloud-based AI, but also a wide range of new security principles to consider.

In this article, I will examine what cloud-based AI means for you as a cybersecurity professional or leader, and how you should adjust to the new landscape.

Firstly, AI and ML have been used by cloud security services for a while

Many cloud services already incorporate some form of Machine Learning (ML) or Artificial Intelligence (AI) to perform security tasks, such as threat detection or sensitive data discovery. Take Amazon GuardDuty, for instance, which leverages ML and threat intelligence to detect anomalies and potential threats across your AWS infrastructure, accounts, and data. Meanwhile, Macie, another AI-powered AWS service, utilizes ML in addition to pattern matching to uncover sensitive information such as social security numbers and phone numbers within S3 buckets. 

That being said, generative AI, in particular, has the potential to completely change the field, propelling security capabilities beyond just threat detection and data discovery. In the face of a growing cybersecurity talent gap, finding qualified professionals, especially those with specialized skills, is a challenge. This is where AI comes into play, closing the gap and equipping entry-level and junior analysts with AI-driven code analysis, incident response guidance, and much more. 

What are the benefits of cloud-based AI for defenders?

AI plays a pivotal role not only in streamlining threat detection processes but also in alleviating security teams from the burdensome nature of monotonous and repetitive tasks. Through the automation of tasks such as log analysis, we can liberate our team from this operational strain, allowing them to redirect their focus towards more critical security events and those that demand specialized scrutiny. This shift in focus enhances our overall responsiveness to emerging threats. 

Furthermore, the automation capabilities extend to the vital area of vulnerability scanning. By implementing automated processes in this domain, we can expedite the identification of potential weaknesses and promptly generate comprehensive reports. This not only boosts the overall efficiency of our team but also ensures a proactive approach to addressing vulnerabilities before they can be exploited. 

An area of considerable advancement is the integration of generative AI for code generation. Given that scripting and coding demand specialized skill sets, which may be lacking in junior security analysts, AI can prove to be an invaluable asset. Through AI-driven tools, analysts can rapidly generate scripts, automating manual processes with precision. Additionally, leveraging AI for code analysis provides the team with a powerful tool to assess whether a piece of code harbors malicious intent, fortifying our defenses against potential security breaches. 

What are the benefits of cloud-based AI for malicious adversaries? 

While AI can bring substantial benefits to security defenders, it also presents a double-edged sword, providing advantages to malicious threat actors. The accessibility of AI tools has empowered novice script-kiddies, granting them the capability to generate malicious code and execute more sophisticated attacks. This democratization of malicious capabilities poses a significant challenge to cybersecurity. 

The evolution of AI in voice technology has had profound implications for social engineering tactics. Malicious threat actors now leverage AI-driven voice synthesis to carry out voice impersonation and vishing attacks. These attacks have become exceptionally challenging to detect, as the synthesized voices closely mimic real human voices. The convergence of AI and voice technology introduces a new layer of sophistication to social engineering, posing heightened risks to individuals and organizations alike. 

Additionally, AI enables malicious threat actors to orchestrate highly convincing phishing email campaigns. The ability to craft emails that mimic legitimate communication is enhanced by AI-driven content generation and personalized targeting. As a result, these phishing campaigns become more deceptive, making it increasingly difficult for traditional security measures to discern the authenticity of the messages. 

What security concerns does AI create?

While the capabilities introduced by AI are undoubtedly impressive, they bring forth noteworthy security concerns, especially in the context of Large Language Models (LLMs). A critical issue is the susceptibility to attacks such as Prompt Injection, a manipulation technique where a Large Language Model (LLM) is tricked through carefully crafted inputs, resulting in unintended actions by the model. Prompt Injection attacks can manifest in different forms, either by directly overwriting system prompts or by indirectly manipulating inputs from external sources. 

Another substantial security challenge is Insecure Output Handling, a vulnerability that emerges when downstream components accept Large Language Model (LLM) output without thorough scrutiny. This vulnerability is particularly concerning when the LLM output is directly passed to backend, privileged, or client-side functions without proper validation, potentially creating avenues for security breaches. 

How can AI security concerns be addressed?

To effectively address these vulnerabilities and safeguard against potential attacks, the implementation of techniques like input sanitation and validation becomes imperative. These measures act as a robust line of defense, ensuring that inputs undergo thorough examination and validation before interacting with downstream components. By doing so, the security posture is strengthened, fortifying defenses against potential threats and exploits. 

Furthermore, organizations must prioritize the protection of their AI applications' data. This involves employing encryption for data in transit, leveraging Transport Layer Security (TLS), and securing data at rest through cloud-based cryptography key management services. Concerning AI model training data, services like Google Cloud’s Sensitive Data Protection offer capabilities for sensitive data discovery, classification, and de-identification, specifically targeting sensitive elements within the dataset. These comprehensive security measures contribute to a more resilient and secure AI ecosystem. 

Conclusion

With all of these Cloud AI security controls methods in mind, you can empower your organization with securing its AI apps. My Cloud AI Security Principles course explains how to secure Cloud AI services, use AI responsibly, and how you can leverage AI to improve your organization's security capabilities.