Introduction
Cloud allows businesses to develop new ideas and grow their workloads and applications to meet the demand of the business. As companies begin to grow and deploy across clouds there are many things that need to be secured. From data and users to applications and workloads your security signals increase as you grow across the cloud. How can we make sure that we know what is a threat and what is normal day-to-day activity.
There will always be a need for traditional security measures but if you want to stay ahead of cybercriminals you need to start looking at what’s causing those alerts. Whether it is a misconfiguration, compromised credentials, abnormal user behavior or vulnerabilities. All of these can leave you open to cyberattacks if you don’t have all the pieces put together.
AI cloud security can help analyze your security signals to help you identify suspicious activity and risk. It can help you respond faster to threats that may occur in your cloud environment. Once you begin implementing AI into your cloud security you will gain increased visibility into what’s happening.
That being said you still need traditional security measures and practices in place. Having good data and security around what your AI can and cannot access is key to utilizing this technology.
Below we will dive into how AI can help with threat detection, compliance and risk in the cloud and steps to help you get started.
What Is AI Cloud Security?
Using artificial intelligence and machine learning to help secure your cloud workloads and data. These can help with your overall security by analyzing large amounts of telemetry to detect abnormal behavior and threats.
If a user is typically logged into a specific application during certain times of day. And all of a sudden that user is logged in and trying to change permissions to a sensitive workload from an unknown environment. Using AI you can detect those signals and analyze them for further investigation.
Here are some examples of how AI can help secure your cloud:
- Identify threats: Help detect abnormal behavior that may be occurring on your cloud workloads and users.
- Identify security posture gaps: Find misconfigurations that may leave you exposed. Detect if any of your workloads are vulnerable and need attention.
- Assist with compliance: Help you identify if you meet the requirements for your set policies. Gather evidence that you can show you are following your policy.
- Assess risk: Can help you identify where your risks are. And give you more information to help decide if you want to act on those risks.
- Help respond to incidents: Once you’ve identified a threat you can use AI to help triage alerts and start to take action to remediate the issue.
All of these can work alongside native cloud security tools and practices. For foundational guidance, read What Is Cloud Security? Definition, Benefits, and Challenges.
The goal is to provide your security team with more information to help make better decisions when responding to threats.
Why Traditional Cloud Security Needs an AI-Assisted Approach
As we continue to grow our workloads across the cloud we need to find ways to keep up with the changes. Whether it is a new workload being deployed. Permissions that are changed, applications updated and so on. There will always be changes that are made that may leave us exposed if we aren’t monitoring for them.
There are many reasons why we need to look at utilizing AI in our cloud security. Here are a few.
1. You Have Too Many Alerts to Keep Up With
You can receive thousands of alerts that are coming from your cloud workloads, endpoints and other areas of your infrastructure. Your security team will need to dig into these alerts and figure out what’s a real threat.
Using AI you can help tie together those alerts that are related to each other. Helping your analyst get a better idea of what may be a real threat and which ones they should dig into first.
2. Limited Visibility Across Cloud Environments
You may have workloads across multiple clouds and different areas of your business. Which can lead to your security data being scattered across different tools and not seeing the big picture.
You can start using AI to pull in those signals and gain what may be a threat. As long as you have your security tools integrated with each other and feeding the correct information.
3. Threats Are Constantly Evolving
Hackers will always be trying to find ways to gain access to your environment. They can steal credentials and use vulnerabilities to their advantage. There will always be new types of threats that come up and your security team needs to stay on top of them.
Using AI you can help detect those abnormal behaviors and start to dig into if it may be a threat. Just remember not everything that is flagged will be a threat.
4. You Need to Stay Compliant
You want to make sure that you have security controls in place that will help keep your data safe. There are many audits that can be done to help ensure you are meeting your policies.
You can utilize AI to help detect if there are any changes that have been made that may not comply with your policies. And help you take action on those findings.
How AI Can Help Detect Threats in the Cloud
Detecting threats can be one of the first things that you start using AI for in your cloud security. It can help you tie together signals that may not seem related at first.
Behavioral Analytics and Anomaly Detection
You can use machine learning to help identify abnormal behavior from your users, applications and workloads. This can help you detect if any data is leaving your environment that may be suspicious or if a user is doing something they shouldn’t be.
behavioral analytics can be helpful if you’re not seeing any signatures that line up with a known attack. But you still need to have the proper steps in place to help investigate if it is really a threat.
Correlating Signals Across Security Tools
If a user fails to log in once that may not be a big deal. But if that user fails to log in and then logs in from a suspicious environment and tries to gain more permissions to a workload that may be a threat.
By using AI you can start to detect these types of signals and help your security team investigate if it is something that needs action. Pulling in logs from different areas of your cloud can also help you identify if there are any suspicious activities.
Alert Prioritization
Not all vulnerabilities and alerts will be the same level of risk. If you have a workload that contains personally identifiable information (PII) you may want to act on those alerts faster than others.
You can use AI to help prioritize those alerts based on information you provide it. Such as if the workload contains personally identifiable information (PII) and what vulnerabilities may affect that workload. You’ll want to make sure you have your criteria set up and can validate the results.
Helping Investigate and Remediate Issues
Generative AI can help you investigate what’s going on when you have a threat. It can help pull together logs and give you a better understanding of what may have happened. You can then use SOAR workflows to start taking actions to help stop the threat.
Just remember you still want to have people in the loop when taking certain actions. You don’t want to have an AI take an action that could potentially harm your business.
Strengthening Cloud Compliance with AI
When it comes to compliance you want to make sure that you have all of your policies in place and are following them. AI can help you detect if there are any areas that you may be falling short on. The Cloud Security Alliance Security Guidance provides broader guidance across cloud architecture, data protection, DevSecOps, Zero Trust, and risk management.
Continuous policy monitoring
Once you have your policies created you can start using AI to help detect if there are any areas that you’re not following them. This can include making sure your workloads aren’t public, encrypted and don’t have too many permissions.
You can have these checks happen when your workloads are deployed and continue to monitor them. AI can help you detect these and tell you what you may need to do to fix the issue. But you still want to have the proper controls in place to make sure these are happening.
Automated evidence collection
You can spend hours trying to gather all the evidence you need to prove you are meeting your policies. With AI and the right tools you can gather this information and have it ready when you need it.
Make sure you have the proper tools in place that will allow you to gather this information and that it’s accurate.
Staying Up to Date on Changes
As you have policies that may change you want to make sure you’re aware of them. Using AI you can help detect if there are any changes that may affect your policies and start to assess if you’re meeting those new requirements.
You shouldn’t be relying on AI to tell you if you’re compliant or not. There are always people that should be reviewing these and making sure you’re meeting your requirements.
The NIST Cybersecurity Framework 2.0 can help organizations structure cybersecurity governance, protection, detection, response, and recovery activities.
Using AI to Improve Your Risk in the Cloud
There are many risks that can occur when you’re running workloads in the cloud. You want to make sure you know where your risks are and what you can do to mitigate them.
Prioritize Vulnerabilities
When looking at vulnerabilities you want to know which ones are more of a risk to your business. You can use AI to help identify which vulnerabilities may be a higher risk based on your workloads and what’s running on them.
User and Identity Risk
You can use AI to detect if there are any abnormal behaviors from your users and identities. If someone is accessing a workload that they shouldn’t be or gaining more permissions than they should have.
Always have MFA and proper access controls in place to help mitigate these types of risks. AI can help detect if something is off but it won’t stop someone from accessing your resources.
Help Protect Your Data
There are many places that your data can live in the cloud. Whether it is databases, S3 buckets or any other area. Using AI you can help detect where your data may be and if it is being protected properly.
Once you know where your data is you can make sure you have the proper security controls in place. Make sure you have encryption in place and are monitoring your data.
Data Protection and Exposure Management
You can use AI to help predict what may happen in the future based on past events. If you’ve had a vulnerability that has been patched you can use that information to help prevent it from happening again.
Always make sure you have people that can verify these predictions and take the necessary actions to prevent them.
What Are Some Risks of Using AI in Your Cloud Security?
While there are many benefits to using AI in your cloud security there are some risks you should be aware of.
You Don’t Have All the Information You Need
If you don’t have all your logs and information in one place your AI may not be able to give you the best results. Make sure you have the proper data in place before starting to use AI.
False Positives and False Negatives
You will always have false positives and false negatives when using AI. You want to make sure you have the proper tests in place to help minimize these.
Your Data Can Get Leaked
If you’re not careful with your AI and the data it has access to you can leak sensitive information. Make sure you have the proper security in place around your AI and what it can access.
AI-Specific Attacks
Just like any other technology AI can be hacked. There are many attacks that can be used against AI and you want to make sure you’re aware of them.
NIST research on AI security and resilience provides additional background on securing AI systems and managing AI-related cybersecurity risks.
Excessive Automation Privileges
If you give your AI too many permissions it can do more harm than good. Make sure you have the proper access controls in place and only give it what it needs.
You Don’t Know Who is Responsible
When you have AI making decisions you want to know who is responsible if something goes wrong. Make sure you have the proper people in place that can oversee your AI and take action if needed.
Steps to Help You Get Started with AI in Your Cloud Security
Here are some steps you can take to help you get started with using AI in your cloud security.
Step 1: Evaluate Your Current Security Posture
Take a look at what you currently have in place. What clouds are you using, what data do you have, what tools are you using and related areas. Identify what areas you may be lacking in and what risks you may have.
Once you have that information you can start to identify what you want to use AI for.
Step 2: Identify Use Cases
There are many use cases you can start with. You can start with alert triage or identify threats that may be happening. Find an area that you know you have data for and can start measuring results.
Step 3: Establish Governance and Guardrails
Make sure you have a plan on how you will use AI and what you expect it to do. Have people that can oversee the AI and make sure it’s doing what it’s supposed to.
Step 4: Integrate AI with Existing Security Operations
Once you have your use cases identified and a plan in place you can start implementing AI into your cloud security. Make sure you have the proper integrations in place so you can start getting data from your tools.
Step 5: Start Testing and Validating Your Results
Make sure you test your AI and validate that it is giving you the correct results. You can do this by using past incidents and see if your AI can detect them.
Once you feel comfortable with your results you can start to scale and implement more use cases.
Step 6: Measure Success
Make sure you have a way to measure if your AI is helping you or not. You can look at how long it takes to investigate an alert or if you are able to detect threats faster.
How Hexaware Can Help
Read how Hexaware’s AI-driven cybersecurity solutions align with NIST CSF 2.0 using Microsoft security services.
See this cloud security and compliance transformation case study for an example of addressing cloud security gaps and compliance requirements.
There are many ways that you can start using AI in your cloud security. It all depends on what you’re looking to achieve and what you currently have in place.
