Certified: The GIAC GPCS Audio Course

In this episode, we take a realistic look at public cloud risk and build a practical map you can carry into any environment without falling into vendor-specific assumptions. The public cloud is not automatically safer or less safe than on-premises, but it is different in ways that change how exposure happens and how quickly it spreads. When people say the cloud is secure by default, what they often mean is that the provider’s infrastructure is heavily engineered, not that your deployment choices are automatically protected from mistakes. A seasoned security perspective is to treat cloud risk as a shifting set of exposures created by configuration, identity decisions, and operational speed, then to map those exposures in a way that stays useful under pressure.

Before we continue, a quick note: this audio course is a companion to our course companion books. The first book is about the exam and provides detailed information on how to pass it best. The second book is a Kindle-only eBook that contains 1,000 flashcards that can be used on your mobile device or Kindle. Check them both out at Cyber Author dot me, in the Bare Metal Study Guides Series.

To map risk well, you need to contrast perceived cloud safety with the realities of shared responsibility duties. The provider does a great deal to secure the physical infrastructure, the underlying hardware, and the foundational services, but your responsibilities do not disappear when you use those services. You are still responsible for how you configure network access, how you manage identities, how you classify and protect data, and how you monitor for misuse. Many incidents happen because teams assume a control is handled by the provider when in reality it is configurable, optional, or dependent on how you integrate it. The shared responsibility model becomes dangerous when it is treated as a slogan instead of a checklist. A practical risk map begins by acknowledging which controls you must implement, maintain, and validate yourself.

Speed and self-service are powerful advantages in public cloud environments, but those same advantages increase the rate of configuration mistakes. In traditional environments, provisioning often requires tickets, approvals, and time delays that unintentionally slow down risky changes. In cloud environments, a developer can create a new service, expose it, and connect it to data in minutes, sometimes without any centralized review. That speed makes organizations more agile, but it also means mistakes can propagate before anyone notices. Self-service can also encourage a mindset where teams view security settings as obstacles rather than as risk controls, especially when deadlines are tight. If you want a realistic map of cloud risk, you have to treat velocity as a risk factor that changes how your controls must be designed.

The most common pattern is not malicious intent; it is rushed work that quietly leaves something open. A team might deploy a workload under pressure, choose permissive settings to make it work quickly, and plan to tighten them later. Later often never arrives, or the change is made without understanding the full blast radius, or the person who planned to fix it changes teams. In cloud environments, a small decision can have a large impact because the platform makes it easy to connect services together and to expose interfaces in standardized ways. Silent exposure happens when the system works as designed but the design was not reviewed through a security lens. A risk map must therefore pay special attention to the default paths of exposure and the places where convenience tempts people into over-permissioning.

Imagine a scenario where a team is racing to ship a feature and they decide to stand up a new storage location for logs and application exports. They pick a setting that makes sharing easy across teams, and they create an access rule that is broader than intended because it eliminates friction. The application passes its tests, the demo looks great, and the deployment is considered a success. Months later, someone discovers that the storage was accessible in a way that allowed data to be retrieved by identities or networks that should never have had access. No alarm fired because nothing appeared broken, and the exposure was not obvious to the people who created it. This is the cloud version of leaving a door unlocked in a building with many entrances, except the building is reachable from far more places than people intuitively understand. The lesson is that cloud failures often look like normal operations until you examine them through the lens of reachability and permission.

High-frequency cloud failure modes across real environments tend to cluster into a few recurring themes, and recognizing them helps you build a map quickly. Overly permissive access controls are one theme, whether they show up as broad identity permissions, wide network access, or services that accept requests from sources you did not intend. Insufficient visibility is another, because teams often deploy workloads without central logging or without consistent monitoring across accounts and services. Weak secrets handling is a common contributor, especially when keys, tokens, or credentials are placed in locations that are too accessible or are not rotated. Misaligned data protections are also common, where sensitive data is stored without appropriate encryption, classification, or access boundaries. Finally, unmanaged drift is a frequent problem, because cloud configurations change constantly, and without continuous oversight, today’s safe configuration becomes tomorrow’s unintended exposure.

A quick win that consistently improves security outcomes is to inventory what exists before trying to secure it. In many organizations, the biggest risk is not that controls are missing, but that leadership does not know what has been deployed and what it connects to. You cannot protect what you cannot describe, and you cannot monitor what you cannot find. Inventory is not just a list of resources; it is an understanding of ownership, purpose, data handled, and exposure paths. When you have even a basic inventory, you can prioritize what to examine first instead of guessing. This also helps you avoid the common pitfall of securing the systems you know about while the most exposed systems remain invisible.

Inventory becomes especially useful when you pair it with one simple practice question: what is internet-facing, and who can change it. This question forces you to identify the interfaces that can be reached from outside your trusted boundaries and to connect them to the identities and workflows that control change. Internet-facing does not only mean a public web server; it includes any service endpoint that can be reached by external actors, any administrative plane reachable from outside, and any data access path that is not properly constrained. Who can change it is equally important because the ability to modify configurations is often the most direct way to create or remove exposure. When you ask this repeatedly, you train yourself to see cloud risk as a relationship between reachability and change authority. That relationship is where most incidents are born.

As you work through that practice question, you will notice that cloud risk tends to revolve around identity more than network boundaries, and that is why identity becomes the new perimeter. In traditional environments, network segmentation and physical boundaries play a major role in determining what an attacker can reach. In cloud environments, identity and authorization often determine what can be accessed even when network paths exist. A compromised identity can call management interfaces, retrieve secrets, change routing, and manipulate security controls, sometimes without touching the same paths an external attacker would use. This is not just about user accounts; it is about roles, service identities, tokens, and credentials used by automation. When identity is treated as a first-class security boundary, you shift from guarding doors to guarding authority.

Identity misuse is also subtle because it can look like legitimate activity. If an attacker uses valid credentials, many security tools will record the actions as authorized, even when the behavior is malicious. That is why identity governance, least privilege, and strong authentication controls are foundational in cloud environments. It is also why monitoring must include behavioral patterns, not just simple allow and deny events. When a role that normally reads a small dataset suddenly enumerates large portions of an environment, or when a service identity begins performing administrative actions it never performed before, that is a signal worth treating seriously. The perimeter is no longer only where packets enter; it is where permissions allow actions. In cloud, permissions are pathways.

Multi-account complexity is another major cloud risk driver, and it multiplies oversight gaps in ways that surprise experienced teams. Organizations often split workloads into multiple accounts or subscriptions to separate environments, business units, or projects, and this can be a strong design choice. The risk comes when governance does not scale with that separation, leading to inconsistent policies, inconsistent logging, and inconsistent identity controls across the estate. Each account becomes its own mini-environment with its own configurations and exceptions. Over time, teams lose a unified view, and attackers benefit from that fragmentation because it increases the chance that at least one account is misconfigured or poorly monitored. Multi-account structures require strong standards and centralized oversight to avoid becoming a collection of blind spots.

The multiplication effect is especially visible in identity and monitoring. If each account has its own roles, permissions, and exceptions, the effective permission graph becomes difficult to reason about without disciplined structure. If logging is not centralized, investigations become slow, and slow investigations are costly in cloud environments where changes can happen rapidly. If network connectivity is established between accounts, a weakness in one account can become a stepping stone to another. Even without direct connectivity, shared identity providers or shared secrets can bridge boundaries unintentionally. Multi-account design can increase security when done well, but it increases complexity every time governance falls behind velocity. Your risk map should explicitly account for how many separate administrative zones exist and whether oversight is consistent across them.

A memory anchor helps you keep cloud risk mapping consistent across environments without getting lost in the details. The anchor for this episode is identity, data, network, and control-plane, and each word represents a bucket you should mentally check as you assess risk. Identity covers who or what can act, what permissions they have, and how those permissions are granted and monitored. Data covers what is stored, how it is classified, who can access it, and what protections like encryption and access boundaries are in place. Network covers reachability, segmentation, and how services are exposed or connected. Control-plane covers the management layer, meaning who can change configurations, deploy resources, and alter security settings. This anchor is effective because it forces you to look beyond a single dimension and to catch the common pattern where one weak area undermines the others.

Now do a mini-review of these four buckets and the failure patterns that appear repeatedly. In identity, the failure pattern is excess permission, weak authentication, and unmanaged credentials that are reused or poorly protected. In data, the failure pattern is oversharing, weak access controls, and lack of visibility into where sensitive information lives and who touches it. In network, the failure pattern is unintended exposure through permissive rules, forgotten endpoints, or connectivity that expands the blast radius. In control-plane, the failure pattern is too many people or systems with the ability to change critical configurations without strong oversight and logging. When you use these buckets, you can quickly diagnose where an environment is most likely to fail, even if you have never seen that particular deployment before. The point is not to find every possible weakness, but to find the high-probability failure paths that create real risk.

Commit to a simple spoken risk map you can reuse when you walk into a new cloud environment. You can say to yourself, identity first, then data, then network, then control-plane, and you can use that order to structure your assessment. As you speak it internally, you attach a few prompts to each bucket, such as who can do what, what sensitive data exists, what is reachable, and who can change the system. This spoken map is useful because it works when you are tired, when you are under time pressure, and when a conversation with stakeholders is moving quickly. It also helps you avoid vendor blind spots, because you are not anchoring on a specific product feature. You are anchoring on universal risk relationships that exist in every public cloud.

To conclude, pick one environment you know, even a small one, and narrate its top risks using the four-bucket anchor. You name the critical identities and the permissions that matter most, and you note where credentials or roles could be misused. You identify the most sensitive data and the paths by which it could be exposed or over-accessed. You call out what is internet-facing and what connectivity expands the blast radius. You finish by describing who can change the environment, how those changes are logged, and where oversight might be thin. When you can narrate those risks clearly, you have built a practical map that works across vendors and avoids the comforting but dangerous assumption that the cloud is secure simply because it is the cloud.