Undeniably, AI systems have a critical role to play when it comes to cybersecurity. Our focus mainly had been on how to secure our cyber system vulnerabilities through ML algorithms. However, there is a flip aspect too. AI systems are known to be vulnerable since they are as good as the data they are trained on. Hence AI systems should be safeguarded throughout their operational lifetime.

Our increasing dependence on AI for critical functions and services will create opportunities for people with malicious intentions. It lures the attackers to target those algorithms and fiddle with them for severe damages or destruction.

The very first point worth understanding is what it means to make AI systems safe and secure.

Nowadays, it is easy for cybercriminals to exploit a software or system's vulnerabilities to compromise the smooth or desired functioning of that system. These adversaries manipulate data sets that are fed to the algorithms or are used as training datasets resulting in faulty outcomes. This is called adversarial machine learning. 

Another similar act is data poisoning, which also occurs when adversaries train AI models on mislabeled data. To make things clearer, we can say even the smallest of changes in digital images that usually might get missed by human eyes mislead AI algorithms and misclassify them. It might be crazy even to imagine what intensity of damage might be caused in such scenarios.

AI is getting so deeply embedded in our lives that we knowingly or unknowingly leave a lot of our critical decision making on these algorithms sometimes. This is why another major risk to AI systems is the opportunity for attackers to compromise their decision-making processes' integrity, leading to undesirable choices. Again this would be either through malicious inputs or through spoilt trains data.

The question here is not just about how AI can be used to augment cybersecurity. However, the question is how AI systems can be trained to secure themselves. The increasing usage of AI in aiding cybersecurity is making it more critical to secure AI systems. Our reliance on ML algorithms for detection and responding to cyberattacks makes it a pressing issue that we protect these algorithms from misuse, interference, or mishandling.

We know that the intense integration of AI in every possible domain, be it finance, healthcare, education, law, manufacturing, etc., is charming attackers to target them for severe consequences.

In the last few years, we have seen swift growth in interest from various nations regarding AI, with more than 27 governments publishing official AI plans or initiatives by 2020. However, most of the focus of these strategies is on increasing funding for AI research, training, skilling, and encouraging economic growth and innovation through the development of AI technologies. This shows that we are missing one vital aspect, i.e., maintaining security for AI.

Few key points to focus on for securing AI:

• Comprehensive documentation
• Regular auditing
• Early and continuous testing
• Policy alignment from both public and private sectors
• Clear guidance to AI developers and users
• Establishing baseline requirements for AI developers
• Certifications for auditing and testing

We need to align our policies to emphasize the crucial aspects such as accountability, testing, robustness, and transparency of the algorithms. It's also important to verify the credibility of the developer of these algorithms as great powers in the wrong hands can be devastating too.

We must build trustworthy AI systems that can be audited through a rigorous, standardized system of documentation. 

In line with that, we need to develop a detailed design documentation process and standards for AI models. This shall take into account what the model uses data and how the models are trained and tested? The auditing and testing shall be extensive and must consider more delicate safety and security risks. 

The AI ecosystem can be secured through a combination of policies, certifications, auditing standards, transparency guidelines, and accountability measures. This endeavour would definitely require collective awareness, expertise, and experience of people in the fraternity. 

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