Operations and infrastructure teams in financial services monitor critical metrics like core banking platform uptime, payment processor status, API gateway latency, mobile app crash rates, and cloud spend. But they often have far less visibility into another crucial source of performance data: the CDN infrastructure that delivers digital services between a bank’s core infrastructure and its customers.
CDNs run on edge infrastructure spread across regions and networks the bank doesn’t control directly. A regional outage, a misrouted request, or a security vulnerability can originate anywhere between the end user and the origin server, and when it does, regulators and customers hold the institution accountable, even if the issue originated in a third-party CDN.
That exposure exists in part because of how CDN logs get classified internally. Authentication events, privileged access records, and transaction trails fall inside audit scope, get captured in full, and follow a retention schedule the compliance team sets. Meanwhile, CDN edge logs typically fall outside that boundary, so they inherit the monitoring team’s policy instead. That means they are usually sampled at ingest, rolled into dashboard aggregates, and aged out after 30 to 90 days. But when an outage or breach occurs, or a regulator requests an audit, the business needs a full record of what happened at the edge. If that record has already been sampled away or discarded, it can’t be reconstructed after the fact.
Why do financial institutions need complete CDN log visibility? Learn more about the business value of complete CDN visibility for financial services.
Sampling Leaves Permanent Gaps in the Record
When CDN log volume outpaces what a monitoring platform can process in real time, most tools fall back on sampling: keeping a fraction of events and discarding the rest. This lowers cost and processing overhead, but it works against operations and security teams during a security investigation or a root cause analysis, at exactly the moment regulatory and compliance pressure is highest.
Some teams try to close the gap elsewhere. A WAF (web application firewall) can be configured to log every request that passes through it rather than only the traffic that trips a rule, but full WAF logging just moves the volume problem to a different system. An API gateway captures every request that reaches it, but requests that fail before triggering an API call don’t appear in gateway logs at all.
CDN logs capture every single request, whether the traffic comes from customers, malicious actors, or bots. That includes failed logins that are part of a credential stuffing attempt, unusually high regional latency on requests that otherwise look legitimate, patterns of bot traffic, and long-running low-and-slow attacks that never trip a WAF rule or reach an API gateway in the first place. When most of that data is sampled away, anomalies and subtle variations in data are often erased, but slow-building attacks or regional degradation often start with these subtler patterns.
Modern data platforms eliminate the need to make this tradeoff. By combining scalable ingestion, high-density compression, and cost-effective object storage, they can retain high-volume CDN logs at full fidelity without the cost and performance penalties that traditionally forced teams to sample or discard data..
Aggregates Capture Metrics But Not Details
Sampling isn’t the only way detail disappears. Pre-built dashboards provided by CDN vendors often show aggregate metrics with no raw log access available. These dashboards can show that the overall network is healthy while smaller regional failures and anomalies are happening. That gap stays invisible until a regulator or a customer asks what happened during an incident, and there’s no record to answer with.
For teams using Akamai, Akamai TrafficPeak provides full-fidelity, complete logs into the edge. Learn more about TrafficPeak.
Consider a dashboard tracking global error rate at 0.3% shows that the overall system is healthy. If that 0.3% is actually a 20% error rate concentrated in one region or PoP (CDN point of presence), that’s a much bigger issue that needs to be addressed.
On its own, aggregate data provides an incomplete picture. If the underlying raw data is sampled, discarded, or otherwise inaccessible, teams lose the ability to drill down into more specific slices, such as region, ISP, or device. They also lose the ability to drill into individual request logs. That includes the kind of detail that shows a single PoP serving stale cache or a specific ASN throwing elevated TLS handshake failures. Is it a ticket that should be elevated to CDN support or something to triage internally? Will it require an hours-long bridge call to piece together incomplete data, or will the raw logs be available to provide additional detail and faster mitigation?
Teams shouldn’t have to choose between aggregate dashboards and raw logs. Instead, modern monitoring platforms provide both: aggregate tables for dashboards and querying, with the underlying raw logs retained and queryable. On high-volume data, dashboards should query aggregate tables to keep performance fast and compute costs down. Queries, whether from agents or human users, can route to aggregate tables when possible and to raw data tables when needed. For CDN logs, that means dashboards for global performance across all CDNs, by individual CDN, and by individual regions and other segments, all querying aggregate tables that are designed to provide accurate data in real time. Meanwhile, the complete raw data is stored in a separate table for forensic analysis and root cause investigation. By keeping raw data, teams can also slice the data in new ways to answer questions they haven’t asked yet. When teams rely entirely on roll-ups, they lose that flexibility.
Short Retention Windows Create Security and Compliance Exposure
Traditional observability platforms rely on fast SSD-based storage to keep queries responsive, but that storage is extremely expensive at scale. When data is kept at full fidelity, there is additional pressure to shorten retention windows or quickly migrate data to frozen storage where it’s difficult to access.
For many types of data, a typical retention window is 30 to 90 days. However, in practice, high-volume log sources are often discarded or migrated much sooner, sometimes in a matter of days. In the case of CDN logs, this provides just enough visibility to monitor edge performance in real time, but it’s a liability for long-term investigations and compliance.
In the case of security, low and slow attacks can occur over months or even years, with the average breach taking 247 days to discover, according to the Cost of a Data Breach Report 2026 from IBM. This timeline extends far beyond typical retention windows. After a breach is discovered, the next step is containment. CDN edge logs can provide a complete request record that helps determine when the attack began, how extensive the attack is, and which attempts were successful and which failed. These records can also support compliance requirements during an active investigation.
The same compression and decoupled storage that makes full-fidelity ingestion cost-effective and performant also makes long-term retention affordable. However, keeping compressed data in object storage without additional optimizations can lead to performance issues. The goal isn’t just cost-effective, long-term storage—if it were, then archived, frozen storage would be sufficient.
Modern solutions use query and storage optimizations to keep data “hot” even when it spans petabytes of data. Techniques like partitioning, parallelism, and high-density compression all make queries on object storage more efficient. Partitioning data by time allows for queries by time range to prune all partitions that don’t include the relevant timestamp, minimizing the amount of data that needs to be scanned. Meanwhile, modern solutions built on cloud services can utilize massive parallelism when needed to scan larger volumes of data. Finally, compression is essential because a 20x compression rate reduces storage costs and also dramatically reduces the amount of data that needs to be scanned and transferred during query time.
Multi-CDN Deployments Lead to Additional Complexity and Visibility Issues
Most financial institutions run more than one CDN provider. The benefits of having additional providers include more effective failover, vendor risk management, cost efficiency, and improved performance. If one provider has an outage, whether major or regional, traffic routes to other CDNs rather than going down entirely.
However, running multiple CDNs leads to additional complexity, and it can lead to gaps in visibility when teams use separate tools and dashboards to monitor each CDN. CDN providers have their own schemas and field naming conventions, making it difficult to compare and correlate data across providers unless data is first standardized at ingest time.
This can be especially challenging during security events and outages. For example, a credential stuffing or DDoS attack may hit multiple providers, or shift from one provider to another during an attack. If traffic fails over from one provider to another during an incident, the question of what actually occurred spans datasets from multiple CDNs. If the data hasn’t been standardized across providers through an extract—transform—load (ETL) process, it needs to be reconciled, standardized, and joined at query time, a time-consuming, often manual process that slows down incident resolution. The problem continues even after the incident is over, when teams must conduct forensic analysis, collect data for compliance records, and put together postmortems.
This problem can be solved by standardizing incoming data across CDNs and ingesting the data into a single table for easier correlations and comparisons across providers. To make dashboards more efficient, aggregate tables can store metrics, leaving the underlying raw data untouched for most queries. Aggregate tables should be available across providers as well as for each provider. This makes monitoring across CDNs much simpler, speeds up incident mitigation, and allows teams to make informed decisions about failover and switching during incidents and outages.
Most Finserv CDN Monitoring Stacks Have the Same Gaps
Financial institutions running general-purpose observability platforms, or juggling multiple CDN dashboards, typically have gaps in the same areas. These questions can help determine whether a CDN monitoring solution is resulting in additional risk to the business.
- When traffic spikes, whether from legitimate volume or an attack, is data full fidelity or sampled? If a spike means losing raw events rather than just paying more to keep them, the platform produces data gaps exactly when teams most need that data for incident detection and mitigation.
- When an incident impacts more than one CDN, can operations and security query a single normalized dataset in real time? If correlating two providers’ logs requires manual work to map and correlate disparate fields, then there is a high risk that reconciliation will slow down incident resolution.
- If a regulator or auditor asks for records from six months ago, can the institution produce them at full fidelity, without a rehydration delay? If the answer depends on how fast someone can restore a cold archive, or the logs aren’t available at all, then the regulatory risk is higher, even if an incident includes CDN logs that don’t have explicit compliance requirements.
The answer to all of the above questions should be “yes.” Modern solutions offer full-fidelity logs, long-term retention, and real-time performance, all while building on commodity object storage to remain cost-effective.
Next Steps
- Interested in learning how Hydrolix can provide full-fidelity, complete CDN visibility? Get a quick value assessment from an expert.
- Already using Akamai? Consider TrafficPeak, an Akamai solution powered by Hydrolix that provides complete CDN observability.
