The Role of AI and Machine Learning in Cloud Security

Cloud computing is a great technology which has redefined the transformation path of Information technology, it has changed the way systems operate, systems are managed, systems are deployed, information is stored and led organizations and its professionals to bring in more innovation and research focusing into security aspects of cloud computing. These clouds are no longer static storage locations for organization data with the introduction of artificial intelligence and machine learning in the cloud domain. What we refer to now is looking at a smarter cloud. 

Today we look more in detail about how AI and machine learning is transforming the way organizations store and process information in cloud computing, its impact, concerns etc. 

How Artificial Intelligence is impacting Cloud Computing?

Artificial intelligence has changed the way data input, storage and analysis happens at a very basic level in cloud computing. The cloud is not merely a cloud storehouse anymore but an ‘intelligent’ storehouse. Machine learning and cloud computing can analyze and learn information and process it at the same time and provide it to other servers and clouds and help in quick decision making. 

Intelligent clouds will be capable of predicting trends based on data ingestion. With the emerging business requirements and embracing cloud computing and artificial intelligence in the core of working methodology. Artificial intelligent supported cloud computing is in its nascent stage as of now, organizations are closely monitoring development in this area to understand its impact. 

Fusion of cloud computing with artificial intelligence is becoming a source of innovation and a means to accelerate change. Artificial intelligence in cloud computing can help users to provide useful responses and data analytics to make cloud computing result and situation oriented.  


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Impact of AI and Machine learning on Cloud Security

With advancements of artificial intelligence, machine learning and deep learning techniques along with the potential of cloud computing offerings services can be more efficient and cost effective. 

  • Machine learning-as-a-service (MLaaS) cloud platforms and increased adoption of third-party cloud services to outsource training of deep learning modules opens a wide range of attack surfaces for vulnerabilities to exploit Machine learning and deep learning systems to gain malicious access. 
  • In Machine learning wherein ML professionals upload their models along with trained data (over cloud) which could lead to several types of attacks to ML ecosystem such as Adversarial attacks, Exploratory attacks, Model extraction attacks, Backdooring attacks, Trojan attacks, Model-reuse attacks, Data manipulation attacks, Cyber-kill chain attacks, membership inference attacks, evasion attacks, model inversion attacks and so on.
  • Leveraging cloud-based ML services for computation enhancements and minimization of communication overheads are promising trends however by sharing the model and training data raises concerns around security and privacy. 
  • Malicious attacks can compromise the model and integrity of data. To avoid such issues users can download models and make local inferences but it has certain drawbacks such as confidentiality, models update etc. adversaries can exploit models and use them to gain access to customer private information hence breaching the privacy. 
  • Techniques like rounding confidence, differential, and ensemble methods, ReDCrypt, image disguising techniques, Homomorphic Encryption, model stacking etc. can help to provide defense against different types of attacks.

AI and machine learning models due to their complexity, have unpredictable behaviour under specific circumstances which introduces vulnerabilities of unanticipated nature. 

  • The ‘black box’ problem has risen due to adoption of AI and machine learning.  
  • One of the most alarming developments in this domain is the use of Artificial intelligence to identify cloud vulnerabilities and create malware. 
  • Artificial intelligence is a potent tool for attackers to automate and accelerate finding vulnerabilities. 
  • Artificial intelligence can be used to analyze patterns, detect potential weaknesses, and exploit them faster before security teams can handle them. 
  • Artificial intelligence’s opaque nature further complicates these security challenges. As AI systems — especially deep learning models.

Measures to tackle AI & Machine learning Security issues in Cloud computing 

  • Implementation of strong access management controls – Adherence to principle of least privileges provide the minimum access required to user and application. Multi-factor authentication needs to be made mandatory for all users. Role based access controls to restrict further access is to be enforced.
  • Leverage encryption – All data needs to be encrypted be at REST or in TRANSIT to protect sensitive information from unauthorized access. Key management processes should be enforced with robustness ensuring regular key rotation management and secure storage of keys. 
  • Security monitoring and intrusion prevention – Ongoing monitoring of cloud environments to identify potential threats help to proactively detect malicious vectors. AI based intrusion detection systems can provide real time analysis of threats. 
  • Vulnerability assessments and penetration testing – Regular vulnerability assessments and pen tests help to identify potential weaknesses in cloud computing environment and help to simulate real world attacks to evaluate organization capability to handle them
  • Cloud native security strategy – Use of cloud native security technologies helps to secure cloud environments. 

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