By Dr Abhinanda Sarkar, Director Academics, Great Learning
The one thing that’s certain in the technological landscape is that any new tool or system is open to threats. To no one’s surprise, bad actors lurk in various corners, figuring out security vulnerabilities they can exploit. While artificial intelligence is certainly transforming our world and driving innovation across industries, its ubiquitous nature makes it a big target. There is potential to misuse and mischaracterise the technology, besides the inherent flaws that leave organisations grappling with security and privacy concerns. Building trust in AI—and how it’s being used—is crucial for wide-scale adoption and smoother integration in daily life.
To reiterate the central idea—AI can be vulnerable. On its own and from outside elements. It can expose security risks that put establishments in places they’d avoid entirely. Both predictive AI systems and generative AI tools are indeed vulnerable to different types of cybersecurity attacks. Various types of poisoning, evasion attacks, and privacy attacks exist, as demonstrated in research and real-world examples. They can not only lead to the manipulation of training data; but also open ways to exfiltrate personal information about people, organisations, and the model itself.
So, what exactly constitutes these attacks? For one, large language models’ (LLMs) behaviour can be altered by prompt injection, leading to model abuse, privacy invasions, and integrity violations. These can be used to create misinformation, commit fraud, spread malware, and much more. Secondly, models can be misled, with users having different methods to go around restrictions and perform unauthorised actions. Poor information filtering of an LLM’s responses or data overfitting during training could lead to leaks of sensitive material. Other concerns include the possibility of exploiting unverified LLM-generated content, improper error handling, and unauthorised code execution, among others. These vulnerabilities could compromise IT teams and their operations by stretching resources in multiple directions, chasing solutions to multi-dimensional problems. Furthermore, these issues could be particularly severe from a cybersecurity perspective. It’s well known how improper training datasets could lead to discriminatory decision-making—these biased algorithms, when used in AI-powered cybersecurity solutions, could overlook certain threats. At the same time, pinpointing how an AI actually arrives at a decision can be difficult. This lack of transparency could challenge security specialists and hinder improvements to systems.
Another critical vulnerability in the cybersecurity discussion could be the lengthy training periods it can take AI models to recognise new threats—this inevitably opens the door to more breaches.
The threats above merely scratch the surface. Bad actors are always finding new ways to exploit technologies or cause malicious actions. AI, being as critical as it is, is an easy target. More specifically, there’s a two-pronged issue to solve. The first is ensuring it is more secure from its intrinsic downsides and external attacks. The second is increasing public trust and faith in AI integrity and addressing privacy concerns. While research answers one concern, the other depends on more transparency in general discourse and prioritising robust security policies.
Circling back to the issue of attacks on AI systems, organisations will need to invest in strengthening their AI capabilities by making room for AI security specialists. These roles will bridge the gap between the technical and administrative sections and smoothen workflows. As the niche grows, having skilled professionals who understand security lapses and can develop countermeasures will be invaluable. Equally invaluable will be leaders who can break down the complex language of AI and simplify it for individuals across the organisation—this demystifies the technology and brings everyone on the same page. More importantly, these leaders should focus on proper workforce training and skilling their security teams to meet industry standards.
Securing AI systems for the long run will thrust cybersecurity in a new direction. Discarding AI bias, defending data in ML operations, protecting against adversarial manipulations, and accounting for the possibility of AI hackers are topics that will capture the imagination of the tech community and cybersecurity specialists everywhere. As AI ambitions meet reality, it becomes a case of maximising operational efficiency for organisations—by harnessing innovation and securing it in and out. AI security will inevitably bloom; prioritising data security, fostering trust, and investing in skilled professionals will ensure the talk is about what AI can do and not what’s wrong with it.
