Source: securityboulevard.com – Author: Kaus Phaltankar
AI is a transformative technology that is reshaping the landscape of security, risk management, compliance and governance in hybrid cloud environments. It is emerging as a powerful force, revolutionizing the way organizations safeguard their digital assets and navigate the complexities of modern computing. Hybrid cloud architectures have introduced unprecedented challenges in maintaining robust security measures, ensuring regulatory compliance and managing risks across diverse infrastructure landscapes. AI is emerging as a crucial ally in this rapidly evolving environment, empowering organizations to proactively identify and address threats, streamline compliance processes and establish robust governance frameworks.
AI has many different dimensions and use cases. It essentially brings more intelligence to how you do things. It builds insights from gathering, assessing and processing large data sets to track the changing risk and compliance posture resulting from changes to the hybrid cloud environment at scale, enabling humans to take accurate actions. This intelligence creates powerful insights that enable organizations to focus on the risks that matter and, as a result, protect their digital assets.
In complex environments with multiple technology stacks at the infrastructure, application and platform levels, both on-premises and in the public and private cloud, there are vast amounts of data sets that are spun up continuously. These complex environments require dealing with change every second, whether with regard to security, compliance or governance posture.
Considering the scale of environments being spun up, AI is slowly but steadily becoming necessary. It helps identify vulnerabilities and misconfigurations in a continuously changing environment at scale (what’s changing, where and what we need to focus on). It’s analogous to sifting through and finding multiple needles in a haystack.
To build an AI-driven compliance, security and governance solution, you must first be able to scale and learn from large data sets. To learn from the data, you must build training models for the data to be processed effectively by the AI component. These training models require the ability to analyze and operate at scale and support different training models for different use cases.
Since we need to analyze and operate at scale continuously, we have moved from the underlying tech of machine learning (ML) to deep learning (DL) based on neural net technology. With this technology, we can detect, analyze and prioritize the findings. The second part of this is auto-remediation; this enables us to understand where the problem is developing and what actions, if taken, would create the biggest impact. This prioritization technique driven by AI and our proprietary technology working together creates a scenario of a self-healing environment. In this environment, a problem is addressed before it becomes a serious issue. It detects any drift in security and compliance postures caused by new application development environments or changes to the existing environment that have occurred based on infrastructure as a code (IaC).
Here are a couple of sample scenarios of vulnerabilities and how AI helps:
A new environment that just got spun up has unencrypted storage volumes: The data is visible and could be compromised if somebody accessed that storage bucket. But if we can quickly encrypt the data using the customer’s key, we have fixed the problem.
Open public access is another situation that can cause information leakage. However, we can quickly remediate the risk by removing open public access and securing the asset.
An endpoint with a malware infection: Because we have already been observing the behavior of various malware infections with AI, we already know one when we see one. This makes it easy to identify a malware infection and quarantine the endpoint in a non-destructive manner so that it does not cause further damage until it’s investigated and remediated. Once again, we have self-healed by stopping the propagation by way of segmenting or essentially isolating that endpoint.
This is how AI becomes a driver of intelligence at scale that allows us to take prudent and measured actions in a non-destructive way to protect and/or heal the environment with auto-remediation actions.
If human intervention is required, we can take the AI’s indicators (set of issues and recommendations to resolve the problems) and feed that into a chatbot. This can help both experts and non-experts address the developing problem.
How AI Addresses Security, Risk, Compliance and Governance in Hybrid Cloud Environments
Threat Detection and Response: An AI-powered platform leverages advanced analytics, machine learning and behavioral analysis to detect anomalous activities and potential threats across hybrid cloud infrastructure. By analyzing massive volumes of data in real-time, AI can swiftly identify patterns, detect vulnerabilities and respond to security incidents, fortifying defenses against sophisticated attacks.
Automated Compliance Monitoring: AI enables automated monitoring and enforcement of compliance policies, reducing manual effort, ensuring consistency and minimizes the risk of human error. Machine learning algorithms can also adapt to evolving regulations, assisting organizations in staying compliant in an ever-changing landscape.
Risk Prediction and Mitigation: AI algorithms can leverage historical data, contextual information, and machine learning techniques to predict and assess potential risks within hybrid cloud environments. By analyzing complex interactions between various components and identifying vulnerabilities, AI empowers organizations to take proactive measures to mitigate risks, enhancing overall security posture.
Governance and Policy Management: The complexity of hybrid cloud environments necessitates robust governance and policy management frameworks. AI provides automation and intelligence to govern access controls, enforce policies, monitor configuration changes and manage data privacy and sovereignty requirements. This enables organizations to maintain a strong governance foundation while achieving optimal resource utilization and cost efficiency.
The potential of AI in security, risk management, compliance and governance within hybrid cloud environments is immense. Its ability to analyze vast amounts of data, adapt to evolving threats and assist in decision-making transforms how organizations protect their critical assets in this dynamic digital era.
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Original Post URL: https://securityboulevard.com/2023/07/ais-impact-on-security-risk-and-governance-in-a-hybrid-cloud-world/
Category & Tags: Analytics & Intelligence,Cloud Security,Cybersecurity,Data Security,Governance, Risk & Compliance,Security Awareness,Security Boulevard (Original),Threat Intelligence,AI,governance,hybrid cloud,risk,security – Analytics & Intelligence,Cloud Security,Cybersecurity,Data Security,Governance, Risk & Compliance,Security Awareness,Security Boulevard (Original),Threat Intelligence,AI,governance,hybrid cloud,risk,security
