Best Practices for Monitoring CDN Security

Content delivery networks (CDNs) are integral to delivering web resources and data efficiently and at scale. For organizations that use them, they also play a vital role in optimizing security.

Most organizations that use CDNs monitor them in real time to track attacks and security risks. However, many organizations aren’t using historical CDN log data to gain insights into vulnerabilities and attack patterns, or to try to proactively detect data breaches and other exploits—a missed opportunity that can lead to significant security gaps.

Making use of that historical data is especially rare for organizations that have very high-bandwidth needs and that use multiple CDN networks. Some media-streaming companies can generate terabytes of log data per day. Many observability and cybersecurity platforms have challenges processing high volumes of data, and even when they do have this capacity, it’s often extremely expensive. For many, that data is dark and mostly unusable.

But there are ways to use that data, and platforms like Hydrolix are making it cost-effective and practical to keep it long term, even at scale. Historical CDN log data can be a treasure trove of actionable, untapped insights. In this post, we’ll explore ways CDN-using organizations can unlock some practical security benefits from their data. This post will be especially useful for high-bandwidth applications, like media-streaming, but it should contain some valuable tips for any organization that relies on CDNs.

Understanding CDNs and Their Role in Security

CDNs enhance application performance by storing copies of your content on geographically distributed servers. This setup reduces strain on individual hosts by allowing multiple servers in various regions to handle incoming requests for cached content. The result? Faster content delivery and an improved user experience. CDNs are especially valuable for transmitting large amounts of data, making them essential for music and video streaming platforms.

Additionally, the distributed structure of CDNs helps mitigate network attacks by decentralizing web processing, allowing for quick traffic rerouting when needed. CDNs also allow for extra layers of security. For example, some have security protocols the primary host lacks.

CDN Security Threats and Costs

As gateways for large-scale content distribution, CDNs are prime targets for cyber attacks. These attacks can lead to severe financial and reputational consequences if not properly managed.

CDNs are vulnerable to a range of cyberattacks, including:

Distributed Denial-of-Service (DDoS) attacks: Overloading CDN servers with malicious traffic to disrupt service.
Cache poisoning: Manipulating cached content to serve malicious data or misinformation to users.
Malware distribution: Exploiting CDN infrastructure to spread viruses and ransomware.
Domain hijacking: Taking control of CDN-linked domains, redirecting users to fraudulent sites.
TLS certificate breaches: Compromising encryption security, exposing sensitive transactions.
Insider threats: Malicious or unintentional actions by employees that lead to data leaks or vulnerabilities (while not often a result of CDN vulnerability, CDNs may lead to more potential exposure).
Improper cache expiration: Failing to refresh or remove outdated content, exposing users to security flaws.

Some of these attacks are aimed at gaining access to personally identifiable information (PII) because that’s generally the most valuable type of data to steal. Other attacks, like DDoS attacks, may be done simply to hurt an organization and damage its reputation. Sometimes, DDoS attacks may be only a distracting camouflage covering up an attempt to steal PII.

Some vulnerability exploits are more subtle, and may only remove some important anonymity from end users. For example, one Cloudflare vulnerability allowed for finding geographic information about the end users of some major products.

Like snowflakes, every attack is different. The more data you have about your CDNs, the more you can understand the attack vectors your organization faces.

Data Breach Costs

Let’s look at data breaches. A single data breach can be catastrophic. According to IBM, the average cost of a data breach in 2024 reached $4.8 million, a 10% increase from the previous year—and the highest ever recorded.

They also found that using AI and automation to optimize security resulted in an average of $2.2 million less in damages, highlighting the importance of striving for security best practices.

The longer a breach goes undetected, the greater the damage. IBM found that in 2024 it took on average 194 days (6.4 months) to identify a data breach—but some breaches can go undiscovered for much longer. During that time, hackers have access to sensitive data, adding to the financial and reputational harm done to an organization.

Then, once discovered, breaches might take a long time to contain: IBM found an average breach in 2024 took 64 days to contain.

Denial-of-Service Attacks

DDoS attacks pose a significant threat to businesses, leading to immediate financial losses and long-term reputational damage. Service disruptions can result in major revenue loss due to customers being unable to complete transactions.

Depending on the industry and scale of the attack, losses can range from thousands of dollars to hundreds of thousands of dollars per hour. One report found the average DDoS attack cost businesses about half a million dollars in 2023.

Additionally, operational disruptions force IT teams to divert resources, increasing costs and reducing efficiency across other areas of the business.

Reputation Harm, Legal Risks, and Insurance Risks

Cyberattacks can severely damage businesses by eroding customer trust, generating negative media attention, and leading to significant financial losses due to lawsuits, legal costs, or regulatory fines. Beyond direct financial costs, companies also suffer reputational damage, as media coverage amplifies public concern and weakens customer confidence.

The financial fallout from cyberattacks extends to stock prices, lost business opportunities, and operational challenges. Harvard Business Review reported that publicly traded companies experience an average stock value drop of 7.5% after a breach, and take over a month to recover. Investors and business partners may hesitate to collaborate with a company that appears vulnerable, leading to lost growth opportunities.

Also, security failures can lower the morale of employees and hurt recruitment efforts.

Cybersecurity insurance can be another area of potential harm. A major security breach can lead to skyrocketing cyber insurance premiums, more scrutiny from insurers, and even policy cancellation. Losing insurance coverage can put a company at great risk; for some organizations, an uninsured attack would present an existential risk.

Analyzing CDN Logs to Detect and Minimize Threats

The volume of CDN data presents both challenges and opportunities for monitoring. The sheer volume of data can provide more opportunity to detect anomalies and use that data for real-time, on-the-fly security adjustments or long-term learnings. The problem, though, is that there can be so much log data that it’s hard to know what to look for. Insights exist but are often hidden in a noisy mountain of data.

When it comes to how organizations make use of that data for security purposes, we can break that down into two general categories:

Real-time or near-real-time uses: Detecting and stopping attacks as they happen.
More long-term analysis: Finding patterns useful for stopping future attacks.

Let’s look at these categories a little more closely.

Real-time CDN Log Analysis

CDN logs can provide real-time insights into network activity, helping organizations detect and respond to security threats as they emerge. Here are some common ways organizations can spot ongoing malicious activity in CDN logs.

Abnormal traffic characteristics: Abnormal patterns can include high traffic from specific IP addresses, traffic containing many invalid or spoofed packets, and surges on specific ports or protocols.
Abnormal geographic traffic: For content that’s expected to have mostly local or regional interest (like a sports game), an abnormal surge in traffic from distant places can be a red flag.
Access from red-flagged locations: Requests originating from IP addresses in embargoed countries or anonymized through TOR networks can be a sign of malicious activity.
Unusual download patterns: When users simultaneously download all available bitrates of a media file, that can indicate piracy or some other malicious activity.
Various known security threats: CDN data flows and logs might highlight patterns related to the OWASP Top Ten vulnerabilities or other known industry-specific threats.

Some high-bandwidth organizations use multiple CDN networks; for example, a media streaming company might use Akamai, Fastly, and CloudFront. This is because they need very high bandwidth and high redundancy. With high redundancy, if there’s a problem with one CDN network, another can provide backup. But the multi-CDN approach can make it difficult to detect malicious patterns in real time or longer-term. This is because some attacks will cross multiple CDN providers, making the attack harder to understand and mitigate if you’re only looking at summaries from each CDN provider separately.

One security best practice for multi-CDN-using companies is to consolidate that data into a single table. This removes the need for costly and time-consuming JOIN operations and allows for broader, improved threat detection. This allows for full, aligned visibility of your entire CDN network surface.

Some high-bandwidth organizations rely on third-party security platforms to analyze CDN data patterns. Large companies sometimes develop their own machine learning (ML) models and algorithms to detect anomalies specific to their industry or organization. For example, they might build their own machine-learning model trained on past attack patterns; that software can then automatically spot ongoing malicious activity and adjust CDN streaming, as needed.

As companies’ bandwidth needs increase over time, and as machine learning improves, it will become an increasingly standard security best practice to make use of historical CDN and network data to automatically detect and mitigate threats.

Long-term CDN Log Data Analysis

As stated, some organizations generate a massive amount of CDN logs (a terabyte per day or more). Few of these organizations keep the raw log data past a few days, if at all. Some may keep only the highest-level summary of that data past a day or so. This is typically due to the fact that the cost of ingesting, storing, and analyzing that data is too high with many platforms, if it’s even possible at all.

Real-time monitoring is obviously essential for catching attacks as they happen. But organizations that only retain CDN logs for short periods of time (or no time) risk missing critical insights. Long-term CDN logs can be valuable for security investigations, anomaly detection, and trend analysis. Here are some reasons why:

Analyzing past security events: Studying past security incidents, such as an attack during a company’s big streaming event a year before, helps prepare for future threats.
User-reported incidents: If end users report a potential security issue days or weeks after it occurs, historical logs help security teams investigate and pinpoint the root cause. Without that historical data, companies are left in the dark about what might have happened.
Uncovering hidden patterns: Sophisticated hackers evolve and adjust their methods over time. Long-term analysis can reveal attack patterns or vulnerabilities unique to an industry or organization.
DDoS attack investigations: During a DDoS attack, an enormous volume of log data is generated (this can sometimes be hundreds of terabytes). Retaining this data, in some form, allows for a deeper post-attack forensic analysis, which can help prevent future attacks.
Detecting rare anomalies: Some threats occur very infrequently, making them hard to detect or analyze when only looking at real-time or recent logs. Paying more attention to these outliers can help you implement more robust security practices.
Investigating known vulnerabilities: When a new security vulnerability or attack vector comes to light, organizations can query their CDN logs to see if they’ve been affected. They might check for specific known patterns involving headers, payloads, cookies, IP addresses, and more. This is one possible way to speed up the process of discovering a data breach.
Legal and forensic investigations. If a breach leads to legal action, long-term logs help track the attackers’ methods, aiding in attribution and financial recovery. The ability to conduct in-depth investigations can help bolster an organization’s reputation for strong security.

How Long-Term CDN Logs Improve Cyber Insurance and Compliance

Businesses with robust CDN log retention practices can strengthen their cybersecurity insurance claims and compliance efforts:

Cyber insurance validation: Insurers may require proof of penetration testing or proactive security measures. Historical logs can confirm that such tests were conducted.
Incident response records: If a breach occurs, logs can provide a clear timeline of events, demonstrating due diligence and proving an organization was invested in security best practices.
Regulatory compliance: Many industries mandate logging retention for compliance with ISO 27001, GDPR, and other security frameworks.

Using Historical CDN Logs in Machine Learning Algorithms

As with the analysis of real-time data, it will become an increasingly standard CDN security best practice to use machine learning (ML) to analyze older CDN logs to learn from patterns and make security adjustments. This includes:

Using data to train security-related AI: The more data available, the better ML models can predict and prevent future attacks.
Precomputed aggregates for faster analysis: Storing long-term logs in an optimized format enables fast historical comparisons without expensive computational overhead.
Pattern recognition across time: Detecting subtle correlations between past security incidents and present anomalies helps organizations proactively strengthen defenses.

Network security teams and hackers are always involved in an ever-evolving cat-and-mouse game. We can be sure that making optimal use of past data patterns will play a major role in most enterprise security operations in the near future.

Store and Query Long-Term CDN Logs

Traditionally, it has been cost-prohibitive to store and query CDN logs (or other data) long-term, and many legacy observability platforms struggle when it comes to ingesting and analyzing the volume of data that CDNs can generate. Hydrolix is designed for real-time analytics at petabyte scale. Because Hydrolix uses decoupled, cost-effective object storage along with high-density compression (10x-50x), enterprises can store data long-term for a fraction of the cost of other platforms. Hydrolix maximizes the performance of object storage, providing sub-second query analytics regardless of whether data is a minute or a year old.

Many enterprises are already using Hydrolix for CDN monitoring—one example includes a major streaming broadcaster monitoring the biggest football game of the year, which had a peak ingestion rate of nearly 11 million rows per second.

With Hydrolix, enterprises can getbetter security insights and operational efficiency without the high costs typically associated with long-term data retention.