AI in the cloud is a disruptive force in cybersecurity, posing new security challenges and providing advantages to both attackers and defenders.

The three most popular areas of technology right now are cybersecurity, cloud computing, and artificial intelligence. What transpires, though, when you mix them all together? By 2024, there will be plenty of chances for security professionals to empower themselves with Cloud-based AI as these areas rapidly overlap, but there will also be a lot of new security concepts to take into account.

In this post, I’ll look at what cloud-based AI means for you as a leader or cybersecurity expert, and how you should adapt to the new environment.

First off, cloud security services have long made use of AI and ML.

Artificial intelligence (AI) and machine learning (ML) are already widely used in cloud services to carry out security functions including sensitive data finding and threat detection. Consider Amazon GuardDuty, which uses threat intelligence and machine learning to identify irregularities and possible threats in your AWS accounts, infrastructure, and data. While this is going on, Macie, another AI-powered AWS service, uses machine learning (ML) in addition to pattern matching to find private data in S3 buckets, including phone numbers and social security numbers.

Having said that, the field could undergo a radical transformation thanks to generative AI, which could advance security capabilities beyond threat identification and data gathering. Finding qualified professionals—especially those with specialised skills—is difficult in light of the widening cybersecurity talent gap. AI can help close this gap by providing junior and entry-level analysts with tools like incident response guidance and AI-driven code analysis.

What advantages does cloud-based AI offer defences?

AI is essential for expediting threat detection procedures and relieving security staff of the tedious and repetitive nature of their work. We can relieve our staff of this operational burden by automating processes like log analysis, freeing them up to concentrate on more crucial security incidents and those that require in-depth examination. This change in emphasis improves our ability to respond to new threats as a whole.

Moreover, vulnerability scanning is one crucial area where the automated capabilities are extended. We can quickly identify potential vulnerabilities and produce thorough reports by putting automated processes in place in this domain. This improves our team’s overall effectiveness while guaranteeing a proactive strategy to fix vulnerabilities prior to their exploitation.

The application of generative AI to code generation is one area that has made significant progress. AI can be a huge help because scripting and coding need specific skill sets that rookie security analysts might not have. Analysts may quickly write scripts and precisely automate manual operations with the help of AI-driven technologies. Furthermore, by using AI to analyse code, the team is able to evaluate whether a piece of code contains malicious intent, strengthening our defences against possible security lapses.

What advantages does cloud-based AI offer to hostile opponents?

While artificial intelligence (AI) can be extremely beneficial to security defenders, it also has a double-edged effect that benefits malevolent threat actors. The availability of AI tools has enabled even the most inexperienced script kiddies to create malicious programmes and carry out increasingly complex attacks. A major threat to cybersecurity is the democratisation of harmful capabilities.

The advancement of artificial intelligence in speech technology has had a significant impact on social engineering strategies. AI-driven speech synthesis is now being used by malevolent threat actors to conduct vishing and voice impersonation assaults. Because the synthesised sounds sound so similar to actual voices, it is now very difficult to identify these attacks. The combination of speech technology and artificial intelligence adds another level of complexity to social engineering, increasing the hazards to both people and companies.

Malicious threat actors can also create incredibly realistic phishing email campaigns with the help of AI. Personalised targeting and AI-driven content development improve the capacity to create emails that look and feel like authentic correspondence. This makes these phishing attempts more convincing, which makes it harder for conventional security procedures to determine the veracity of the communications.

What kind of security issues does AI raise?

Although AI has clearly produced astonishing new capabilities, it also raises important security issues, particularly when it comes to Large Language Models (LLMs). One crucial concern is the vulnerability to assaults like Prompt Injection, a manipulation technique in which a Large Language Model (LLM) is misled by carefully constructed inputs, causing the model to do unintentional actions. Attacks using Prompt Injection can take many different forms, such as outright replacing system prompts or subtly modifying input from outside sources.

Insecure Output Handling is a significant security issue that arises when downstream components accept Large Language Model (LLM) output without conducting a comprehensive analysis. This vulnerability is especially worrying since it could open the door for security breaches when the LLM output is provided unchecked straight to privileged, client-side, or backend services.

How might security issues with AI be resolved?

The use of strategies like input cleanliness and validation becomes essential in order to resolve these vulnerabilities and protect against future assaults. These controls serve as a strong protective barrier, guaranteeing that inputs are thoroughly inspected and verified prior to engaging with components further down the chain. By doing this, defences against possible attacks and exploits are improved and the security posture is strengthened.

Additionally, businesses need to give data security for their AI applications first priority. This entails using cloud-based cryptographic key management services to secure data at rest, utilising Transport Layer Security (TLS), and encrypting data in transit. With regard to training data for AI models, services such as Google Cloud’s Sensitive Data Protection provide sensitive data discovery, classification, and de-identification capabilities, focusing on sensitive aspects within the dataset. An AI environment that is more safe and robust is enhanced by these extensive security measures.