There are four Prisma Cloud systems to bring online and operationalize: Vulnerability management, compliance, runtime defense, and firewalls. Each system should be operationalized in stages:
Stage 1:Configure the feature. Turn on what you need. All systems ship with sensible default rules.
Stage 2:Observe the incoming data (audits, reports, etc), and get familiar with the feature and its capabilities.
Stage 3:Put your policy into place one step at a time. Tighten your rules in measured steps, giving all teams time to acclimatize at each step.
When Defender is installed, it automatically starts scanning images, containers, and hosts for vulnerabilities. Study this data first before configuring any additional vulnerability scanning. Next, configure Prisma Cloud to scan your registries. Then install and configure the Prisma Cloud Jenkins plugin. If you use CI tool other than Jenkins, then use twistcli, a stand-alone command line utility, to scan the images emitted from the build. Finally, configure Prisma Cloud to scan your serverless functions.
Prisma Cloud can gate both the CI and CD segments of your pipeline. Images are built in the CI segment of the pipeline. After an image is built, Prisma Cloud scans it for vulnerabilities according to thresholds defined in your policy. If the image passes the scan, it is promoted to the registry. If the image doesn’t pass the scan then the build is failed.
In the CD segment, the orchestrator pulls updated images into the production to execute. Here again, Prisma Cloud scans images for vulnerabilities according to thresholds you set in your policy before they run. If they comply with your policy, they’re allowed to execute. If not, an alert is raised, and the container is optionally blocked from running.
There are three points where Prisma Cloud can block a container:
- Build time:Prisma Cloud scans images after they’re built to ensure they comply with your vulnerability and compliance rules. If not, the build is failed (blocked).
- Before the container runs:Prisma Cloud scans images before they runs to ensure they comply with your vulnerability and compliance policies.
- When the container is running:If an anomaly is detected, where a container’s activity deviates from its known baseline activity, the Prisma Cloud runtime defense system can block (stop) the container.
Why are there separate gates for the CI and CD segments? The CD gate protects against drift over time. An image deemed clean by the CI scanner today can be pushed to the registry. However, a few weeks later, new threat data might uncover a Critical severity vulnerability in an image previously deemed clean, and that should raise an alert.
The following matrix shows the starting point for your vulnerability management policy. In the beginning, you’ll want to minimize any disruption to your pipeline. Simply raise alerts in the CI and CD segments, and observe the affect. Set the severity threshold to Critical in your Prisma Cloud vulnerability management policy to limit the scope to just the most important CVEs.
Then start clamping down the CI segment by blocking (failing) builds that emit non-compliant images. Ease into blocking by only failing builds when Critical vulnerabilities have vendor fixes. Set up grace periods so that devs have time to mitigate issues before builds are hard failed. Then lower the threshold from Critical to High to fail builds that emit images with both Critical and High vulnerabilities.
Next, configure blocking in the CD segment to prevent non-compliant images from executing in your prod environment. Although many customers strive to achieve blocking policies in both the CI and CD, everyone has a different comfort level. Your desired end-state might be to block in CI, but only to alert in CD.
For blocking in the CD segment, follow the same recipe as the CI segment. Start with easy achievable goals and slowly ratchet up. Blocking in the CD requires mature processes and trust in the Prisma Cloud Intelligence Stream. You need to satisfy yourself that Prisma Cloud’s threat feed minimizes false positives, such that blocking policies only trigger when there are, in fact, issues to be addressed.
Prisma Cloud has hundreds of compliance checks Most of them are based on the CIS Benchmarks, including the Docker Engine benchmark, Kubernetes benchmark, and Distribution Independent Linux benchmark. Our security research team graded each check (Critical, High, Medium, and Low) so that you can focus on the issues most likely to expose your environment tp a direct attack from someone on the outside. The default rule sets all Critical and High checks to alert, and all Medium and Low checks to ignore.
When deploying Prisma Cloud, you need to tune the default compliance policy to suit your environment. For example, many orchestrator components require more privileges than the average containerized app. Prisma Cloud has a check, which we graded as High, for containers running as root. Some orchestrator containers, however, must run as root in order to function, so you’ll have to add rules that explicitly exempt these containers.
Review all your compliance issues in Compliance Explorer. For every 100 compliance issues, you should expect:
- 67% of issues are caused by your apps. Since you have full control over your apps, meet with your developers and remediate them.
- 33% of issues are caused by infrastructure issues.
- Half of these issues will be due to the vendor configuration, which you can’t change. Create rules to ignore these issues.
- The other half will be due to insecure defaults. Remediate them.
Runtime defense automatically models the intent of a container image so that it can be secured at runtime.
Models are composed whitelist rules that govern what the container can do, based on what Prisma Cloud has learned about its baseline behavior. In most cases (90%), Prisma Cloud builds models that fully capture the container’s intent. For the remaining cases (10%), the models do not fully account for everything the container does at runtime. For these cases, you must manually augment the model.
When you leave models untuned, you can get thousands of spurious audits. Your goal is to eradicate false positives and get your environment quiet. There should be no spurious audits. Spurious audits obscure alerts from truly anomalous, malicious activity, which you do want to monitor closely.
Most of the work to clean up spurious audits is front-loaded when an app is first deployed. When tuning models, focus on a single app at a time. Use the table filters in Console to show audits for just a single app. Sit down with the developer and review the audits to identify what behaviors the model hasn’t captured, then x the model. You have two choices for fixing a model: relearn or add a rule. In most cases, relearn the model to fix the issue. Relearning opens the model to new information about how the running container behaves. In a few cases, relearning simply cannot account for all the activity inside the container. In such a case, create a new runtime rule to augment the model.
You should create as few rules as possible. As a rule of thumb, for every 100 image models, you’ll need about 5 rules to augment the models.
Prisma Cloud Cloud Native Firewalls learn the network topology of your applications and provide application-tailored micro segmentation for all your microservices. Prisma Cloud offers two types of firewalls to protect your applications at the container level.
CNAF is a layer 7 web application firewall (WAF) If you have a container that handles web requests, configure CNAF to protect it. Initially, set CNAF to alert and monitor CNAF audits for rogue requests. Analyze rogue requests, then create rules to specifically block those types of requests.
CNNF is a layer 3 firewall that automatically models inter-container traffic. As part of the automatic behavioral learning at runtime, Prisma Cloud builds out a topology of connections from one container to another. Initially, set CNNF to alert. After you’ve tuned your runtime models, set the action in your policy to "Prevent" to drop attempts to establish connections not whitelisted in the learned models.
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