What is AI security?
AI security is a key component of enterprise cybersecurity that focuses on defending AI infrastructure from cyberattacks. Focusing on AI security is vital because numerous AI technologies are woven into the fabric of organizations. AI is the engine behind modern development processes, workload automation, and big data analytics. It’s also increasingly becoming an integral component of many products and services. For example, a banking app provides financial services, but AI-powered technologies like chatbots and virtual assistants within these apps provide an X factor.
The global AI infrastructure market is forecast to reach more than $96 billion by 2027. According to McKinsey, there was a 250% rise in AI adoption from 2017 to 2022, and the most prominent use cases included service operations optimization, creation of new AI-based products, customer service analytics, and customer segmentation. Unfortunately, every single one of these AI use cases is susceptible to cyberattacks and other vulnerabilities.
That’s just a tip of the iceberg. Data engineers and other agile teams leverage GenAI solutions like large language models (LLMs) to develop applications at speed and scale. Many cloud service providers (CSPs) offer AI services to support this development. You may have heard of or used AI services like Azure Cognitive Services, Amazon Bedrock, and GCP’s Vertex AI. While such services and technologies empower teams to develop and deploy AI applications faster, these pipelines introduce numerous risks. The bottom line is that AI is not quite as secure as many believe, and it requires robust fortifications.
How (un)secure is artificial intelligence?
The narrative surrounding AI often focuses on ethics and the possibility of AI replacing human workforces. However, Forrester claims that the 11 million jobs in the US that will be replaced by AI by 2032 will be balanced by other new work opportunities. The relatively overlooked complexity is at the crossroads of AI and cybersecurity. Threat actors leverage AI to dispense malware and infect code and datasets. AI vulnerabilities are a common vector for data breaches, and software development lifecycles (SDLCs) that incorporate AI are increasingly susceptible to vulnerabilities.
GenAI, in particular, poses many risks. The dangerous potential of GenAI is seen in tools like WormGPT, which is similar to ChatGPT but with a focus on conducting criminal activity. Luckily, the application of AI in cybersecurity is being used to ward off such threats with ChatGPT security evolving. The AI in cybersecurity market will reach $60.6 billion by 2028, proving that human security teams will struggle to identify and remediate large-scale cyberattacks facilitated by AI without utilizing AI themselves.
Cybersecurity AI will continue to play a large role in combating AI-powered security threats. It’s important because threat actors will use LLM prompts as a vector to manipulate GenAI models to reveal sensitive information. CSPs are likely to fully embrace the AI revolution soon, which means that significant infrastructure and development-related decisions will be facilitated by AI chatbots. The use of chatbots as weapons (like WormGPT or FraudGPT) suggests that companies will have a lot of unpredictable AI-related cybersecurity challenges to reckon with.
It’s important to remember that AI can be secured. However, it’s not inherently secure.
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