Certified: The CISSP Prepcast

Welcome to The Bare Metal Cyber CISSP Prepcast. This series helps you prepare for the ISC squared CISSP exam with focused explanations and practical context.
In this episode, we explore Emerging Technologies and Security Architecture, focusing specifically on the Internet of Things and Artificial Intelligence. These innovations are reshaping how organizations operate, how data is processed, and how security must be implemented. As technologies evolve, so do the threats and the architectural decisions required to defend against them.
Let’s begin with a broad understanding of emerging technologies in the cybersecurity space. The term “emerging technologies” generally refers to innovations that are rapidly gaining adoption but have not yet reached full maturity in security practices or regulatory frameworks. These include smart devices, machine learning, automation platforms, blockchain applications, quantum computing, and cloud-native infrastructure. In today’s episode, we’ll focus on the Internet of Things and Artificial Intelligence, because they represent two of the most impactful—and vulnerable—developments in modern cybersecurity.
The Internet of Things refers to physical objects equipped with sensors, software, and connectivity that enable them to collect and exchange data. These devices are integrated into everything from thermostats and refrigerators to manufacturing equipment and medical monitors. While this connectivity enhances convenience, efficiency, and operational insight, it also expands the attack surface exponentially. Every connected device becomes a potential point of entry into the network.
Artificial Intelligence refers to the use of machines and algorithms to simulate human reasoning and learning. In cybersecurity, this is often expressed through machine learning models that detect patterns in data, identify anomalies, and automate responses to threats. But AI is a double-edged sword. While it can greatly enhance our ability to defend systems, it can also be used by attackers to bypass controls, adapt to defenses, and carry out sophisticated campaigns at scale.
As these technologies become embedded into organizational environments, security architecture must evolve to accommodate them. The goal is not only to protect the technologies themselves but also to ensure that their integration does not compromise the broader system.
Let’s now turn to the specific challenges introduced by the Internet of Things. The most obvious issue is the sheer number of devices. In a typical enterprise environment, you might have thousands of connected devices—from printers and badge readers to industrial sensors and employee wearables. Each of these devices needs to be secured, managed, and monitored.
Many IoT devices lack basic security features. They may ship with hardcoded passwords, use outdated firmware, or lack the ability to receive security updates. Some do not support encryption or even basic authentication. These weaknesses make IoT devices prime targets for exploitation. A compromised device can be used to exfiltrate data, serve as a launchpad for lateral movement, or participate in distributed denial-of-service attacks.
To secure IoT environments, organizations must apply strong access control and device authentication. Devices must be assigned to tightly controlled network segments, separated from sensitive data and core systems. Firewalls, intrusion detection systems, and monitoring tools must be configured to watch IoT traffic specifically, as it often behaves differently than traditional enterprise traffic.
Organizations must also enforce secure device onboarding procedures. Before any device is connected to the network, it must be verified, registered, and configured in accordance with security standards. If the device cannot be secured, it should not be allowed to connect.
The use of encryption is critical. Data transmitted from IoT devices must be encrypted in transit. Sensitive information, such as medical readings or surveillance footage, must also be encrypted at rest. Where possible, organizations should choose devices that support secure firmware updates and require signed code to prevent tampering.
Let’s now turn our attention to Artificial Intelligence. Within cybersecurity, AI is often used to enhance detection, automate responses, and reduce the time it takes to identify and contain threats. Machine learning models can be trained to recognize abnormal behavior, flag suspicious login patterns, and even prioritize alerts based on severity.
These tools can significantly reduce the burden on security analysts and improve the organization’s ability to respond to threats in real time. But they also come with risks. Adversarial machine learning is a field where attackers deliberately manipulate input data to deceive AI models. For example, an attacker might craft network traffic that appears normal to the algorithm but actually contains malicious payloads.
Another concern is the transparency of AI systems. Many machine learning models function as black boxes, providing results without clear explanations. This lack of interpretability can lead to blind spots in incident response and limit the ability to demonstrate compliance or explain decisions to stakeholders.
To safely integrate AI into your security architecture, you must apply rigorous validation processes. Models must be trained on clean, representative datasets. Outputs must be monitored for bias, drift, or inconsistencies. Controls must be in place to prevent automation from acting without human oversight in sensitive scenarios.
Organizations must also prepare for the ways AI will be used against them. Malicious actors are already developing AI-driven tools for phishing, credential stuffing, and vulnerability scanning. The same automation that helps defenders can be used to scale attacks with greater speed and accuracy.
For more cyber-related content and books, please visit cyberauthor dot me. You'll find best-selling books, training tools, and resources tailored specifically for cybersecurity professionals. You can also explore more CISSP support at Bare Metal Cyber dot com.
Let’s now look at how organizations can design effective security architectures that incorporate emerging technologies like IoT and AI. Begin with formal documentation. Policies and procedures should define how new technologies are selected, deployed, and secured. They should specify who is responsible for maintaining devices, models, and systems, and how those responsibilities are enforced.
For IoT, security architecture must include strong device identification, encryption of all data flows, and secure configuration management. Devices must be segmented into isolated zones with controlled communication paths. Default settings must be changed, and unused services should be disabled.
For AI, architecture must include layers of oversight. AI models should not be the sole authority for decision-making in high-risk areas. Outputs must be logged, monitored, and audited. Developers must maintain visibility into how models function, what data they process, and how they respond to different types of input.
Monitoring and logging are crucial. IoT and AI systems generate enormous volumes of telemetry. This data must be captured, analyzed, and correlated across systems to detect potential threats. Logging also supports forensics, compliance, and ongoing risk assessment.
Penetration testing and red team exercises are also vital. Organizations must regularly test the resilience of their IoT deployments and AI defenses against realistic attack scenarios. These exercises reveal configuration weaknesses, model vulnerabilities, and architectural blind spots that may not appear in routine audits.
Let’s conclude with how organizations can maintain continuous improvement in their security architecture as technologies evolve. Emerging technologies are not static. Threats will change, use cases will expand, and regulatory expectations will grow.
Your security architecture must evolve just as quickly. Regularly review and update your standards, configurations, and controls. If an IoT vendor issues a security advisory, act on it. If a new AI tool becomes available, evaluate its fit against your risk tolerance and mission needs.
Incident analyses are an excellent source of insight. If an IoT breach occurred, why did it happen? Was the device exposed to the internet? Was there insufficient monitoring? Was the firmware outdated? Use those insights to harden systems and close gaps.
Collaborate across departments. IT, engineering, compliance, and operations must work together. Emerging technologies touch every corner of the organization. Only by collaborating can you ensure that security is consistent and effective.
Invest in training. Your teams must understand how to deploy, manage, and protect these technologies. That includes technical skills, policy awareness, and threat modeling. Training must be updated frequently and tied to real-world use cases.
And finally, stay proactive. Join information-sharing communities. Monitor threat intelligence feeds. Participate in standardization initiatives for emerging technologies. By staying ahead of the curve, your organization can use innovation to its advantage—without sacrificing security.
Thank you for joining the CISSP Prepcast by Bare Metal Cyber. Visit baremetalcyber.com for additional episodes, comprehensive CISSP study resources, and personalized certification support. Deepen your understanding of Emerging Technologies and Security Architecture, and we'll consistently support your journey toward CISSP certification success.