Artificial Intelligence is a rapidly progressing field with potential deployments in various sectors like financial services, trading, healthcare, translation, transportation, image recognition, and more.

As of late, AI systems are deemed to be high-performing and secured. To date, much of the discussion related to machine learning had to linger around identifying the potential threats to computer systems and protecting the same against these vulnerabilities through automation, etc.

However, on the other hand, there had been many concerns; too, as AI was used for offensive purposes, making cyber attackers manipulate the adaptation to malware programs.

Many enterprise policymakers who work in artificial intelligence think of the impact of AI in security administration. The latest report back in 2020 by the UK Artificial Intelligence Commissionerate specified incorporating AI into the cyber defense for proactive detection and mitigation of threats.

This approach requires a greater speed of response than the actual human decision-making process. There are many aspects that AI can take care of if you can implement it accurately. 

While exploring this further, we can see a distinct set of issues that deal with how the AI systems may work securely, and not just about how this can be used to augment the data security and computer networks.

If we rely on machine learning algorithms to detect and respond to cyber threats, what is more, important is that these algorithms need to be protected from any compromise or misuse. As there is an increasing dependence on AI for many crucial services and functions, it will offer a greater incentive for cyber attackers to target such algorithms.

Implementing AI solutions may also respond to the rapidly evolving threats, which will further put forth the need to secure the enterprise systems. So you need to be aware about what you are using how it will eventually benefit your operations to make it better.  

AI has become a very important and widely used technology in many industries, so security policymakers may find it necessary to consider the intersection of AI with cybersecurity. This article will discuss some of the challenges in this area, including compromised decision-making and the AI systems being used for some malicious purposes. For secured database administration services, RemoteDBA.com is a reliable service provider to go for.

Securing the Artificial Intelligence decision-making process

One of the biggest security threats to AI systems is its potential for accommodating any adversaries to compromise the integrity of the decision-making process. With this, the decisions and choices may not be made properly as the user desires.

One easy way to achieve this may be taking control of the system directly so that they will be able to decide what output the system may generate and what decision it will come up with. Alternatively, an attacker may also try to influence these decisions indirectly by providing some malicious inputs or adversary training to the data of an AI model.

As a real-time example, we can consider adversary training to compromise the functioning of an autonomous vehicle so that it may get into an accident. An attacker can exploit the vulnerabilities in the software of the car to influence the self-driving decisions externally.

By exploiting the software and remotely accessing it, one may make the car avoid a stop sign. The computer vision algorithms may not be able to recognize the stop sign as it is. This process with which an adversary can influence a system to make mistakes and manipulate the inputs is called ‘adversarial machine learning.

Research had done this to find that small changes to the digital images that are undetectable to human eyes can be sufficient to cause an AI algorithm to misclassify the images completely.

Data poisoning

Another approach to manipulate the inputs is through ‘data poisoning.’ This, too, can occur when the adversaries try to train the AI models on any mislabeled or inaccurate data. The pictures of stop signs can be labeled as something different, so the algorithms may not recognize the stop signs when encountering them in real-time.

Data poisoning may lead to algorithms making mistakes and doing misclassification of inputs. Even on trying to train an AI model selectively, a specific subset of the data that is labeled accurately may be sufficient to compromise the model to make inaccurate or unexpected decisions.

These types of adversities may reaffirm the need to carefully control both the training data sets and those used to build AI models. The inputs of such models may then be provided to ensure the security of machine learning decision-making processes. Neither of these may be straightforward. Inputs to the AI machine learning system, in particular, may go far beyond the scope of the control of AI developers.

On the other hand, the developers may typically have much greater control over the training data sets for their models. In many cases, these data sets may contain personal data or sensitive information too, which may raise another concern about how this information can be protected. All these concerns may create some tradeoffs for the developers about how the training can be done and how much access to data they have by themselves.

Research done on adversarial machine learning shows that AI models needed to be more robust to data poisoning and adversarial inputs in building foolproof AI systems. These should reveal more information about individual data points also to train the models. While the sensitive data is used to train such models, it will create a new set of security risks that the adversaries will access.

Trying to secure the AI models from these inference attacks may leave these data sets more susceptible to adversarial machine learning techniques and vice-versa. This means that a part of maintaining security for artificial intelligence is to navigate all the tradeoffs between these two related sets of risks.

Once you understand these underlying risks at the intersection of AI with cybersecurity, you need to be very careful about the same impact in your specific use case and take appropriate measures to tackle the same.

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