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Cyber Security Cloud Managed Rules vs Lightning AI comparison

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Review summaries and opinions

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Categories and Ranking

Arctera Insight Platform
Sponsored
Average Rating
0
Number of Reviews
0
Ranking in other categories
Data Governance (61st), Compliance Management (31st)
Cyber Security Cloud Manage...
Average Rating
8.0
Number of Reviews
8
Ranking in other categories
AWS Marketplace (12th)
Lightning AI
Average Rating
8.6
Number of Reviews
2
Ranking in other categories
AWS Marketplace (71st)
 

Featured Reviews

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AmitRathod - PeerSpot reviewer
Senior Analyst at Toll Holdings Limited
Automated cloud security has protected web apps and APIs while enforcing least privilege access
The best features of Cyber Security Cloud Managed Rules include core features that extend protection specific to CMS platforms such as WordPress and Joomla or framework exploits. These rules are regularly updated against the latest CVEs, botnets, and malware without requiring human intervention. I can specify the range in the system and it will update against the latest botnets or malware without requiring any human intervention. It also includes a specialized rule set which mitigates threats targeting web applications, APIs, and serverless environments. Cyber Security Cloud Managed Rules help in managing vulnerabilities such as SQL injections and XSS by modeling user and device behavior across the entire cloud estate. If an admin suddenly logs in from a suspicious location or a script makes unusual API calls, Cyber Security Cloud Managed Rules instantly isolates the resource or flags it for review. When a particular threat is recognized, Cyber Security Cloud Managed Rules bypasses the need for human intervention to execute defensive actions such as changing firewall modes, blocking malicious IP addresses, or revoking compromised credentials. I have CI/CD deployment pipelines which are assigned strict scoping to IAM roles, so these roles have the precise permissions required to push updates to Cyber Security Cloud Managed Rules via infrastructure as code. This ensures that every deployment is pre-validated and consistent with the organization's security policies. I assess the impact of continuous updates provided by threat intelligence as significant because I have set a particular number of alerts. Whenever I see that this alert is triggered, I conduct monitoring. Based on the manage rules defined in AWS and whatever protocol enforcement exists, my model detects and blocks unauthorized access which deviates from standard HTTPS or other standard protocols, and it mitigates sophisticated bot and malware attacks. API gateway or serverless stage is specifically tuned to block API security and serverless threats. These rules can be natively deployed in front of CloudFront or application load balancer and API gateway. For enhanced operational control, these managed rules are commonly paired with WAF-Champ or automated WAF operation service which helps to manage exception and false positive rates. The detailed logs and analytics from Cyber Security Cloud Managed Rules help when it comes to making informed security decisions, and it totally depends on the decisions and what logs are needed. As long as I have defined all logs related to sign-in logs and audit logs, based on the logs, I decide whether to go with those alerts or whether to minimize that security risk and where to focus.
Shravan Revanna - PeerSpot reviewer
Product Engineer at a non-profit with 51-200 employees
Rapid experimentation has transformed our AI prototyping and collaboration workflows
There are definitely a few areas where Lightning AI can improve. Overall, we have had a positive impact, but there are definitely a few areas it could enhance. One area is cost visibility and resource management. There are multiple teams running experiments, GPUs, and long-running sessions. It is not always obvious how much compute is being consumed and what the projected costs might be. More granular visibility and alerts would help the team manage usage proactively. Another area is workspace and project organization. As the number of experiments grows, it can become difficult to keep projects, notebooks, data sets, and test environments organized. Better lifecycle management could help achieve this and discoverability would be useful for larger teams. We have also encountered situations where long-running sessions or development environments needed more resilience. While this is not unique to Lightning AI, interruptions during model training and experimentation can be frustrating, especially when working with larger data sets. From an enterprise perspective, I think there is room to strengthen governance and operational control. Features around permissions, auditability, environment standardization, and usage policies become increasingly important as adoption expands across teams. I would particularly appreciate better support for moving successful experiments into production workflows. There could be better cost and resource visibility, stronger project and experiment organization, improved reliability for long-running sessions, stronger governance capabilities, and a smoother journey from experimentation to production. None of these are major blockers for us, but these are areas where the platform could become more valuable as the team and workload scale. A minor annoyance would be stronger project and experiment organization. When more data sets and more projects come into place, it becomes difficult to organize, and keeping them in a standardized way becomes slightly difficult. That is an area I wanted to highlight. There is not much of a pain point. There are a few minor suggestions I would mention, such as observability and experiment tracking at scale. When teams start running many experiments across different models, it becomes increasingly important to have a clear view of what changed and why performance improved or declined. That could be one area. Another area is cross-team discoverability. As AI adoption grows within an organization, valuable experiments and reusable components can be scattered. Better mechanisms for surfacing reusable workflows and templates would be beneficial. I would also appreciate continued investment in LLM and agent development workflows. The AI landscape is evolving rapidly. These suggestions come from the perspective of a team that is using the platform heavily. Most of the core capabilities work well today, which is why the feedback is more about helping the platform scale with a growing AI organization rather than fixing major shortcomings.
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Top Industries

By visitors reading reviews
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Construction Company
30%
Logistics Company
14%
Outsourcing Company
8%
Manufacturing Company
7%
Construction Company
38%
University
15%
Manufacturing Company
9%
Outsourcing Company
6%
 

Company Size

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Large Enterprise
Midsize Enterprise
Small Business
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By reviewers
Company SizeCount
Small Business4
Large Enterprise8
No data available
 

Questions from the Community

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What is your experience regarding pricing and costs for Cyber Security Cloud Managed Rules?
Regarding my experience with pricing, setup costs, and licensing, this was not handled by my team because we have a s...
What needs improvement with Cyber Security Cloud Managed Rules?
Cyber Security Cloud Managed Rules needs improvement in such a way that whenever the application team or development ...
What is your primary use case for Cyber Security Cloud Managed Rules?
My main use case for Cyber Security Cloud Managed Rules is to protect from malware like DDoS incidents, against cross...
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