In my opinion, a capitalistic economy is not particularly bad to society. The case of India is a good example where it has both worked for and against it, or, what people say, a double-edged sword.

On one hand, it rapidly fuels the economic growth which in turn creates jobs and ultimately boosts living standards for many. This led to a entrepreneurial growth which now shows in – numerous startups along with innovative small businesses in all over India.

A live example can be shown with the rise of tech giants like Infosys and TCS which is a testament to the entrepreneurial spirit unleashed by market-driven forces. Where millions are lifted out of poverty, while the middle class expanding significantly.

Even so, on the other hand, the chase for profits is worsening the already existing social inequalities.

Which means, the gap that has been between the rich and poor has widened more. Leaving a small section of the population with accumulating wealth while a large section struggles with poverty.

Furthermore, this economic imbalance leads to challenges in areas like education, healthcare, and infrastructure, specifically in the rural India.

Okay see, AI can be a tool for security but it’s not invincible so fully relying on AI for security can be risky.

Now, let’s understand the risks associated with using AI in cybersecurity with an example – Imagine AI as a digital guard, protecting your digital fortress, it identifies and defends your system from any cyberattacks.

But even AI has its weakness just like a human guard. The AI systems can be tricked by Hackers who might find loopholes in the AI’s defenses, or even teach it to act in harmful ways.

These are the following risks associated with AI-

1. Adversarial Attacks

AI can be tricked. An attack called AI poisoning where hackers can trick AI systems into making mistakes. Other than that with Evasion Attacks they find sneaky ways to bypass its defenses. And the most common of them is Deepfakes where AI is used to create fake content which seems real leading to reputation damage.

2. Errors and Bias

See, sometimes, AI systems make errors, that is called AI Hallucinations where it can mistake a harmless file for a virus and vice versa. This can lead to false alarms or missed threats.

AI can be biased. If the data used to train an AI is biased, the AI might make unfair decisions. In relation to that there is another problem called Black Box problem when the reasoning behind AI decisions is challenging to understand.

3. Dependence

Too much reliance on anything is risky. It’s the same with AI as well, while it is helpful, it’s important to check it’s work under human expertise.

Now, there’s another concern with the use of AI that might take humans jobs. And as AI is getting better, that is a valid reason to worry.

So, at last these are the few things to keep in mind while using AI safely:

1. Making sure that AI systems are trained on fair and unbiased data.
2. By regularly testing AI systems to find and fix mistakes.
3. Combining human expertise with AI to make better decisions.
4. And to be aware of the potential risks and limitations of AI.