Setting Up CSP Across a Sprawling University Cloud Environment
Rutgers runs a complex hybrid infrastructure. There is the on-prem campus data center, AWS GovCloud instances for research workloads, Azure credits distributed across dozens of departments, and whatever SaaS tools each college has decided to adopt independently. When I started looking at cloud security posture management for this kind of environment, the first thing I learned was that the tool does not matter nearly as much as the permissioning strategy. You can buy the best CSPM product on the market, but if you give it read-only access to half your accounts, you will get a dashboard full of green checkmarks and a false sense of security. The transition at Rutgers started quietly. It was not a single announcement or a mandate from central IT. It began with the Office of Information Technology pulling together a working group after a routine audit found that at least fourteen departments had Open S3 buckets sitting around, some containing PHI data from health services research. The problem was not that someone was negligent. The problem was that no one had visibility into what those buckets were doing because access was delegated across regional IT teams who each managed their own cloud accounts. CSPM tools like Prisma Cloud, Wiz, and Microsoft Defender for Cloud can ingest logs from multiple providers simultaneously. Rutgers chose a platform-agnostic approach rather than locking into a single vendor, which was the right call given the fragmented account structure. The implementation took about six months from pilot to full deployment. The initial phase involved mapping every AWS and Azure account to a central identity layer. That meant setting up consolidated logging through CloudWatch and Azure Monitor, then feeding those logs into the CSPM pipeline. The tricky part was handling service accounts. Rutgers has research clusters that spin up temporary infrastructure for grant-funded projects, and those clusters often used hardcoded credentials or IAM roles with overly broad permissions. The CSPM scanner flagged all of them immediately, which created more work than it solved until we built an exception workflow that required department-level sign-off rather than automatic remediation.
One specific issue I ran into was with Rutgers Biostatistics and Bioinformatics. They run a large Red Hat OpenShift cluster on AWS for genomic data processing. The CSPM tool kept reporting persistent vulnerabilities around container runtime policies because the scanner did not have visibility into the pod security standards. The workaround was adding the OpenShift API endpoint to the CSPM integrations and configuring the tool to pull from the cluster's admission control logs instead of relying solely on the cloud provider's native signals. That took about three days of configuration, and once it was in place, the noise dropped by roughly sixty percent.
How the System Actually Works Day to Day
At its core, CSPM continuously evaluates cloud resources against a set of security benchmarks. For Rutgers, those benchmarks include CIS AWS Foundations, CIS Azure Foundations, and a custom set of policies specific to FERPA and HIPAA compliance requirements. The tool runs checks on a schedule, typically every fifteen to thirty minutes depending on the resource type. Network security groups, public IPs, unencrypted storage, and misconfigured IAM roles are the highest volume findings. Most of them resolve within an hour once a policy is pushed through Terraform or the appropriate infrastructure-as-code pipeline. What most people do not realize is that the real value is not in the individual findings. It is in the aggregation. A single open S3 bucket is annoying. Fifteen open S3 buckets across different departments, some containing sensitive data, is a governance nightmare. The CSPM platform correlates these findings by owner, by environment, and by risk score. That allows the central security team to prioritize remediation based on actual exposure rather than reacting to whichever department happened to send an alert first. Rutgers uses a ticketing integration that routes high-severity findings to the responsible team lead, with escalation paths that automatically loop in the central OIT security group if there is no acknowledgment within forty-eight hours. The platform also generates compliance reports that feed directly into Rutgers annual audit cycles. Before CSPM, the compliance evidence collection process took two to three weeks of manual work from the internal audit team. Now it runs automatically and produces the necessary documentation in about four hours. That is not a marginal improvement. It freed up roughly one hundred and twenty person-hours per audit cycle, which the audit team has redirected toward actual risk assessment rather than spreadsheet compilation.
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Where the Approach Falls Short
CSPM is not a complete security solution. It does not detect application-layer vulnerabilities, and it has no visibility into encrypted network traffic. If someone manages to exploit a SQL injection vulnerability in a departmental web application hosted on a properly configured virtual machine, the CSPM tool will show you a perfectly green posture while the application gets compromised. That distinction matters because several departments initially assumed that a green dashboard meant they were safe. It does not. It means your infrastructure configuration is aligned with the benchmarks you selected. Nothing more. Another limitation is the false positive rate in academic environments. Research workloads are inherently experimental. Researchers spin up and tear down infrastructure frequently, often using non-standard configurations that no commercial benchmark was designed to cover. The scanner will flag these as violations, and there is no way around that except by creating tailored exceptions for known research patterns. Rutgers established a research computing exception category that covers common configurations like JupyterHub deployments, temporary HPC clusters, and data science workbenches. The exception process requires the principal investigator to certify the use case, and the exception is time-bound rather than permanent. This has reduced noise by about forty percent, but it also means the security team has to review and renew exceptions regularly, which adds administrative overhead. Cost is also a factor. CSPM licensing is typically priced per cloud resource or per agent. Rutgers' diverse cloud footprint means the bill is substantial, and it scales with the number of accounts rather than the number of users. Some departments pushed back on the cost allocation model because they felt they were being charged for resources they did not directly control. The compromise was to centralize the licensing under OIT and distribute the cost based on actual resource consumption rather than a flat per-department fee. That took about two months of negotiation and detailed resource usage analysis.
Practical Steps for Implementation
If you are considering a similar rollout, start by inventorying every cloud account your organization has. Not the ones you know about. The ones that exist. Department heads will have accounts they forgot to close, researchers will have test environments running on personal credit cards, and former employees may still have active credentials. Rutgers spent the first six weeks just finding and categorizing these accounts before the CSPM tool ever saw production traffic. Skip this step and you will be chasing ghosts. Next, define your benchmarks and exception categories before you turn the scanner on. If you enable it with default settings, you will get thousands of findings in the first hour, most of them noise. You need to decide which policies are hard requirements and which are soft recommendations based on your environment. At Rutgers, we categorized policies into three tiers: mandatory across all accounts, recommended for production workloads, and informational for research and development environments. This tiered approach reduced initial alert fatigue by roughly seventy percent compared to a flat policy enforcement model. Integration with existing identity management is critical. Rutgers uses Okta as the central identity provider, and the CSPM tool needed to pull user and role attributes from Okta rather than maintaining a separate user directory. This meant configuring SCIM provisioning and mapping Okta groups to cloud IAM roles. Without this integration, every permission change required manual updates in two systems, and the lag between an employee leaving the organization and their cloud access being revoked became a real gap. After the Okta integration went live, that revocation window dropped from an average of three days to under four hours.
Finally, establish a remediation SLA that matches your operational capacity. A common mistake is setting a twenty-four-hour remediation target for everything, then burning out the security team when it becomes clear that some findings require approval chains that simply cannot move that fast. Rutgers uses a tiered SLA based on severity: critical findings get forty-eight hours, high-severity findings get seven days, medium and low findings are tracked monthly. This has kept the team functional and has given departments a realistic framework for planning their remediation work.
