Unifying Cyber Defenses: How Hybrid Mesh Firewalls Shape Modern Security

The traditional castle-and-moat model of cybersecurity is outdated due to the evolving perimeter caused by remote work and fluid data access. Organizations must integrate security at every touchpoint. The proliferation of IoT devices increases entry points for cybercriminals, necessitating a unified approach to endpoint security.

Advanced technologies like AI and quantum computing are transforming cybersecurity, making threats more sophisticated and encryption standards vulnerable. The convergence of technologies, such as networked sensors and big data, expands the attack surface while improving AI capabilities for both attackers and defenders. The increasing sophistication of cyberattacks, as seen in incidents like the SolarWinds hack and Colonial Pipeline attack, highlights the need for proactive, integrated security strategies.

Critical infrastructure vulnerability, regulatory considerations, and the necessity of collaborative security practices underscore the importance of a Unified Security Platform to provide adaptive defenses and foster a security-conscious culture within organizations. The Hybrid Mesh Firewall emerges as a vital component in this landscape, offering the flexibility and comprehensive protection required to meet modern cybersecurity challenges. Before we delve into “What is Hybrid Mesh Firewall”, let us discuss a few customer problems:

Key problem areas for customers

1. Misconfigurations and vulnerability exploitation

One of the most significant issues plaguing organizations is the prevalence of misconfigurations and the exploitation of these vulnerabilities. Despite having multiple security products in place, the risk of human error and the complexity of managing these systems can lead to critical security gaps.

2. Rapid attack execution

The speed at which cyber-attacks can be executed has increased dramatically. This necessitates even faster defense responses, which many traditional security setups struggle to provide. Organizations need solutions that can respond in real-time to threats, minimizing potential damage.

3. Hybrid environments

The modern workforce is distributed, with employees working from various locations and using multiple devices. This hybrid environment requires robust protection that is enforced as close to the user or device as possible. The conventional approach of backhauling remote user traffic to a central data center for inspection is no longer viable due to performance, scalability, and availability constraints.

The emergence of SASE has transformed how network and security solutions are designed, providing connectivity and protection for a remote workforce. However, the shift to distributed controls has become inevitable, presenting its own set of challenges. Many customers deploy best-of-breed security products from different vendors, hoping to cover all bases. Unfortunately, this often results in a complex, multi-vendor environment that is difficult to manage.

4. Siloed security management

Managing security across different silos, with multiple teams and solutions, adds to the complexity. Each system must operate effectively within the principles of Zero Trust, but ensuring consistent performance across all products is challenging. Security systems need to work cohesively, but disparate tools rarely interact seamlessly, making it hard to measure and manage risks comprehensively.

The hybrid mesh firewall solution

Hybrid mesh firewall platforms enable security policy enforcement between workloads and users across any network, especially in on-premises-first organizations. They offer control and management planes to connect multiple enforcement points and are delivered as a mix of hardware, virtual, cloud-native, and cloud-delivered services, integrating with other technologies to share security context signals.

By unifying various firewall architectures, Hybrid Mesh Firewalls ensure consistency and coherence, proactively identifying gaps and suggesting remediations for a holistic approach to network security.

Benefits of hybrid mesh firewalls

1. Unified security management: By consolidating various security functions into a single platform, Hybrid Mesh Firewalls simplify management and reduce the likelihood of misconfigurations. Administrators can oversee and configure all aspects of network security from one place, ensuring that no critical security gaps are overlooked.
2. Proactive threat identification and remediation: The platform continuously monitors the network for vulnerabilities and misconfigurations, such as when a team managing the Secure Service Edge (SSE) solution inadvertently allows direct access to a risky file-sharing site. In such cases, the firewall promptly alerts the admin and provides a remediation flow, ensuring only low-risk apps access the internet directly while other traffic is securely tunneled. This proactive approach prevents incidents before they occur, safeguarding the network from potential threats like data exfiltration or malware infiltration.
3. Real-time response: With the capability to respond in real-time to threats, Hybrid Mesh Firewalls ensure that security measures keep pace with the speed of attacks. This rapid response capability is crucial for minimizing damage and maintaining business continuity.
4. Zero trust enforcement: Each component of the security system operates independently but within the overarching principle of Zero Trust. This means that the endpoint protection software on a remote user’s device functions correctly, regardless of the firewall configuration at the data center, and vice versa. Every element of the security infrastructure works to ensure that trust is never assumed and always verified.

Beyond remote work: Securing workloads everywhere

The need for robust security extends beyond the realm of remote work. Modern organizations are leveraging a mix of private and public cloud environments to run their workloads. Whether it’s a private data center, a public cloud provider like AWS or Azure, or even multiple public clouds, the security landscape becomes increasingly complex.

Hybrid Mesh Firewalls are designed to secure workloads regardless of their location. This approach ensures that security policies are consistently applied across all environments, whether on-premises, in a single public cloud, or across multiple cloud providers.

Securing hybrid workloads:

1. Consistent policy enforcement: By providing a unified platform, Hybrid Mesh Firewalls ensure that security policies are consistently enforced across all environments. This eliminates the risk of discrepancies that can arise from using different security products in different locations.
2. Integrated visibility and control: With integrated visibility into all network traffic, Hybrid Mesh Firewalls allow administrators to monitor and control security policies from a single interface. Centralized management is crucial for identifying and mitigating risks across diverse environments.
3. Scalability and flexibility: As organizations grow and their infrastructure evolves, Hybrid Mesh Firewalls offer the scalability and flexibility needed to adapt to new requirements. Whether adding new cloud environments or scaling up existing ones, the firewall platform can grow with the organization.

Conclusion

The need for Hybrid Mesh Firewalls has never been more critical. As organizations navigate the complexities of a distributed workforce, hybrid environments, and the ever-evolving threat landscape, a unified, proactive, and real-time approach to network security is essential. Hybrid Mesh Firewalls offer the consistency, control, and comprehensive protection needed to secure modern hybrid environments effectively. By addressing the key problem areas of misconfigurations, rapid attack execution, and siloed security management, they provide a robust solution that meets the demands of today’s cybersecurity challenges and beyond.

Source: cisco.com

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The AI Revolution: Transforming Technology and Reshaping Cybersecurity

Artificial Intelligence (AI) is revolutionizing government and technology, driving an urgent need for innovation across all operations. Although historically, local and state government systems have seen only incremental changes with limited AI adoption, today, a significant shift is occurring as AI is integrated across all government sectors.

Benefits of AI Integration

The benefits of these changes are evident. AI-powered systems analyze vast amounts of data, offering insights for better decision-making. Public services become more personalized and efficient, reducing wait times and enhancing citizen satisfaction. Security is significantly bolstered through AI-driven threat detection and response. Consequently, governments are adopting AI and advanced software applications to provide secure, reliable, and resilient services to their citizens, enhancing digital engagement and communication within their communities.

With this rapid growth, cybersecurity operations are among the areas most significantly impacted by advancements in artificial intelligence. CyberOps is at a unique intersection, needing to leverage advanced AI capabilities to enhance effectiveness and resiliency. However, numerous applications and connections are simultaneously challenging it by utilizing emerging AI capabilities to improve their effectiveness and resilience. Despite historically being rigid and resistant to change, CyberOps must adapt to the challenges of an AI-driven digital world.

Whole-of-State / Agency Cybersecurity Approach

Whole-of-State cybersecurity and zero trust governments can be challenged with maintaining digital operations while ensuring sensitive information’s privacy and security. Cisco’s technology allowed agencies to easily meet these requirements through advanced AI-powered security solutions and privacy-preserving AI models. Thanks to techniques like federated learning and differential privacy, sensitive information could be processed and analyzed without compromising individual privacy.

Adopting AI-Driven Services

Adopting AI-driven, easily consumable, on-demand services provides a secure, sustainable, and reliable foundation to build on. Investing in an infrastructure that is secure and flexible allows governments to quickly pivot to the emerging opportunities that the AI revolution brings. No one person could have predicted or prepared for such a transformative shift. Still, the ability to rapidly adapt to the challenges it brought and continue to serve the community and citizens in the ways they deserve is key.

Challenges and Adaptation

Don’t be mistaken, change is often hard. Humans are creatures of habit and comfort and rarely like to be pushed outside our comfort zone. Unfortunately, the AI revolution is doing just that. It is forcing us to adapt and discover new ways to operate and provide what are now seen as even the most basic digital services. The drive and demand for AI-powered services in the government sector are rapidly expanding. We are experiencing one of the most significant catalysts for technological adoption in the state and local government space since the internet became mainstream.

This revolution is driving the necessity for a whole-of-state cybersecurity and zero trust approach. The goal is no longer maintaining the status quo but rather achieving a level of service that provides the foundation for how things can be in an AI-enabled world. Providing enhanced, secure services and support to the community has become the resounding focus of state and local governments.

Cisco’s Role in Supporting Governments

As we navigate this AI revolution, Cisco stands ready to support governments in their journey towards whole-of-state cybersecurity and zero trust adoption. Our comprehensive suite of AI-powered solutions provides the building blocks for a secure and efficient AI-enabled government infrastructure. The shift to a more inclusive, AI-driven government began with specific applications but is rapidly expanding to all sectors and offerings in the state and local government spaces.

Source: cisco.com

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Digital Forensics for Investigating the Metaverse

The intriguing realm of the metaverse should not make us overlook its cybersecurity hazards.

Metaverse adoption has been steadily increasing globally, with various adoption use cases such as virtual weddings, auctions, and the establishment of government offices and law enforcement agencies. Prominent organizations such as INTERPOL and others are investing considerable time and resources researching space, underscoring the importance of the metaverse. While the growth of the metaverse has been accelerating, its full potential has not yet been realized due to the slow development of computing systems and accessories necessary for users to fully immerse themselves in virtual environments, which is gradually improving with the production of augmented reality and visual reality solutions such as HoloLens, Valve Index and Haptx Gloves.

As virtual reality tools and hardware evolve, enabling deeper immersion in virtual environments, we anticipate a broader embrace and utilization of the metaverse.

Significant concerns have risen regarding criminal activity within this virtual realm. The World Economic Forum, INTERPOL and EUROPOL have highlighted the fact that criminals have already begun exploiting the metaverse. However, due to the early stage of the metaverse’s development, forensic science has not yet caught up, lacking practical methodologies and tools for analyzing adversarial activity within this realm.

Unlike conventional forensic investigations that primarily rely on physical evidence, investigations within the metaverse revolve entirely around digital and virtual evidence. This includes aspects such as user interactions, transactions and behaviors occurring within the virtual world. Complicating matters further, metaverse environments are characterized by decentralization and interoperability across diverse virtual landscapes. There are unique challenges related to the ownership and origin of digital assets as users can join metaverse platforms with their anonymous wallets and interact with them pseudonymously without revealing their real identity. Such analysis requires advanced blockchain analytics capabilities and large attribution databases linking wallets and addresses to actual users and treat actors. As a result, this new digital realm necessitates the development of innovative methodologies and tools designed for tracking and analyzing digital footprints, which play a crucial role in addressing virtual crime and ensuring security and virtual safety in the metaverse.

The security community needs a practical, real-world forensic framework model and a close examination of the intricacies involved in metaverse forensics.

Case studies

User activity in the metaverse is immersed in digital environments where interactions and transactions are exclusively digital, encompassing different moving parts such as chatting, user movements, item exchanges, blockchain backend operations, non-fungible tokens (NFT), and more. The diverse and multifaceted nature of these environments presents adversaries with numerous opportunities for malicious activities such as virtual theft, harassment, fraud, and virtual violence, which will only be exemplified with the development of more realistic metaverse environments (Figure 1). The distinct aspect of these crimes is that they often lack any physical real-world connection, presenting unique challenges in investigating and understanding the underlying tactics, techniques and procedures leveraged by adversaries.

Occurrences of threats in metaverse platforms already exist, with the most notable to date involving the British police launching its first ever investigation into a virtual sexual harassment in the metaverse, stating that although there are no physical injuries, there is an emotional and psychological impact on the victim.

Figure 1. INTERPOL’s outline of potential threats in metaverse.

Here are two other theoretical scenarios that exemplify the importance of metaverse forensics, and the need to distinguish their differences from contemporary forensics.

Scenario 1 – Robbery from an avatar (a metaverse gift): In the metaverse, a character approaches another avatar to present virtual shoes as a gift. The avatar accepts the gift, but a few hours later discovers that all digital assets associated with their metaverse account and digital wallet have disappeared. This incident involving stealing digital assets occurred because the seemingly innocent gift of virtual shoes was, in fact, a malicious NFT embedded with adversarial code that facilitated the theft of the avatar’s digital assets.

Scenario 2 – A metaverse conference: A user attends a cybersecurity conference in the metaverse, not knowing it is organized by cybercriminals. Their aim is to lure high-value stakeholders from the industry to steal their data and digital assets. This event takes place in a well-known conference hall in the metaverse. The registration form for the event includes a smart contract designed to extract personal information from all attendees. Additionally, it embeds a time-triggered malicious code set to steal digital assets from each avatar at random intervals after the conference ends. Investigating such incidents requires a comprehensive multi-dimensional analysis that encompasses marketplaces, metaverse bridges, blockchain activities, individual user behavior in the metaverse, data logs of the conference hall and the platform hosting the event, as well as data from any supporting hardware.

Challenges for forensic investigators and law enforcement

Several challenges exist for metaverse investigators. And as the metaverse evolves, additional challenges are expected to surface. Here are some potential issues law enforcement and cybersecurity investigators may run into.

Decentralization and jurisdictions: The decentralized nature of many metaverse platforms can lead to jurisdictional complexities. Determining which laws apply and which legal authority has jurisdiction over a particular incident can be challenging, especially when the involved parties are spread across different countries. As such, it will be exponentially complex or even impossible in some cases for law enforcement to subpoena criminals or metaverse facilitators.

Anonymity and identity verification: Users in the metaverse often operate in an anonymous or pseudonymous manner with avatars with random nicknames, making it difficult to identify their real-world identities. This anonymity can be a significant hurdle in linking virtual actions to criminals. Only few options for unmasking adversarial activity exist, including tracing IP addresses and analyzing platform logs which can be a complex undertake when dealing with truly decentralized metaverse platforms, often leaving blockchain analytics as the only viable analysis methodology.

Complexity and interpolarity of virtual environments: The metaverse can contain a myriad virtual spaces, each with its own set of rules, protocols and types of interactions. Understanding the nuances of these environments is crucial for effective investigation. To compound on the complexity of virtual environments, many metaverse platforms are interconnected, and an investigation may need to span multiple platforms, each with its own set of data formats and access protocols.

Digital asset tracking: Tracking the movement of digital assets, such as cryptocurrencies or NFTs, across different platforms and wallets through blockchain transactions requires specialized knowledge and tools. Without such dedicated tools, tracing digital assets is impossible as such tools contain millions of walled address attributions, ensuring the effective tracing of funds and assets.

Lack of international standards: The absence of global standards for metaverse technology development allows for a wide variety of approaches by developers. This diversity significantly affects the investigation of metaverse platforms, as each requires unique methods, tools and approaches for forensic analysis. This situation makes forensic processes time-consuming and difficult to scale. Establishing international standards would aid forensic investigators in creating tools and methodologies that are applicable across various metaverse platforms, streamlining forensic examinations.

Blockchain immutability: The immutable nature of blockchain ensures that all recorded data remain unaltered, preserving evidence integrity. However, this same feature can also limit certain corrective actions, such as removing online leaks or inappropriate data and reversing transactions involving stolen funds or NFTs.

Correlation of diverse data sources: Data correlation plays a crucial role in investigations, aiming to merge various data types from disparate sources to provide a more comprehensive insight into an incident. Examples of that can be correlating the events of different systems or combining end-host data with associated network data or the correlation between different user accounts. In the context of the metaverse, the challenge lies in the sheer volume of data sources associated with metaverse technologies. This abundance makes data correlation a complex task, necessitating an in-depth understanding of diverse technologies supporting metaverse platforms and the ability to link disparate data sets meaningfully.

Lack of forensic automation: Investigators commonly use various automated tools in the initial stages of their forensic analysis to automate various pedantic operations. These tools are crucial to identify signs of compromise efficiently and accurately. Without these tools, the scope, efficiency, and depth of the analysis can be greatly impacted. Manual analysis requires more time and heightens the risk of overlooking critical signs of compromise or other malicious activities. The emerging and complex nature of metaverse environments currently lacks these tools, and there is no anticipation of their availability soon.

Metaverse investigation approach

The forensic approach for the metaverse is distinct from traditional approaches, which typically begin with investigations focusing on physical devices for telemetry extraction. Investigating the metaverse is a challenging task because it involves more than just examining various files across multiple systems. Instead, it requires the analysis of diverse systems within different environments and the correlation of such data to draw meaningful conclusions.

An example illustrating metaverse forensic complexities is, a rare digital painting, goes missing from a virtual museum. A forensic system should undertake a comprehensive investigation that includes reviewing security logs in the virtual museum, tracing blockchain transactions, and examining interactions within interconnected virtual worlds and marketplaces. The investigation should also analyze recent data from devices like haptic gloves and virtual reality goggles to confirm any malicious related user activities. The analysis of virtual logs or hardware is dependent on the logs recorded by providers or vendors and whether such logs are made available for analysis. If such information is not present, there is little that can be done in terms of forensic analysis.

In this example, if the metaverse platform and virtual museum did not maintain logs it would be impossible to verify the activities preceding the theft, including information about the adversary. If logs from haptic gloves or reality googles are also not present, the activities described by the user during the adversarial activity would have been impossible to verify. This leaves a forensic investigator unable to perform in-depth analysis apart from monitoring on-chain data and the transfer of the painting between the museum wallet and adversarial wallet addresses.

Metaverse platforms vary in their approach to logging and data capture, significantly influenced by the method through which users access these environments. There are primarily two access methods: through a web browser and via client-based software. Web browser-based access to metaverse platforms, like Roblox and Sandbox, requires users to navigate to the platform using a browser. In contrast, client-based platforms such as Decentraland necessitate downloading and installing a software application to enter the metaverse. This distinction has profound implications for forensic analysis. For browser-based platforms, analysis is generally limited to network-based approaches, such as capturing network traffic, which may only be feasible when the traffic is not encrypted. On the other hand, client-based platforms can provide a richer set of data for forensic scrutiny. The software client may generate additional log files that record user activities, which, alongside conventional forensic methods like analyzing the registry or Master File Table (MFT), can offer deeper insights into the application’s use and user interactions within the metaverse. Regardless of the access method, the potential for forensic analysis can be further expanded based on the types of logs and data recorded by the metaverse environment itself and made available by the provider. This means that within each metaverse platform, the scope and depth of forensic analysis can vary based on the specific logs kept by the environment, offering a range of analytical possibilities.

Forensic systems suited for metaverse environments should start their investigation in the digital realm and use physical devices for their supporting data. These forensic systems must connect to user avatars, their accounts, and related data to facilitate initial triage and investigation. Forensic solutions for the metaverse should be capable of conducting triage, data collection, analysis and data enrichment, paralleling the requirements for examining current software and systems. The following three features would greatly benefit forensic investigators when analyzing the metaverse:

1. Triage collection: Collection of forensic artefacts start within the metaverse environment or platform, extending to other supporting software and hardware devices enabling users to interface with the metaverse.

2. Analysis: Processing the captured data to link relevant data and activity based on the reported incident aiming to identify anomalies and indicators of compromise (IOCs). Machine learning can be leveraged to automate the investigation by analyzing relevant telemetry based on the reported indicators of compromise or incident outcomes according to similar past incidences and the analysis and resolution provided by forensic analysts.

3. Data enrichment: Based on the IOCs identified, forensic systems must be capable of searching diverse sources such as blockchains, metaverse platforms and other associated information to identify relevant data for added context.

Forensic systems for the metaverse should be able to directly interact with a user’s avatar (Figure 2), which may adopt a non-player character (NPC) for assistance. When activated, the NPC avatar should be able to engage with the user’s avatar, requesting access to the avatar’s data, the metaverse platform, and all associated software and hardware implicated in an incident. This includes the metaverse console, IoT devices, networking devices and blockchain addresses. To ensure enhanced privacy and security, NPC forensic analysts should only be able to access user data if they are only activated or requested by a user and should only obtain read-only access.

The forensic NPC avatar should meticulously record relevant logs and document any detected indicators of compromise (e.g., suspicious metaverse interactions) along with the observed impact (e.g., NFT or crypto token theft) and the estimated timeframe of the incident from the user’s avatar. Given the inherent complexity of metaverse environments, these forensic systems should possess the ability to operate on multiple layers to gather data, among others:

1. Blockchain to analyze transactions and exchanges performed on-chain.

2. Metaverse Bridges to analyze activities across linked metaverse environments.

3. Metaverse Platforms, including different apps and digital assets in the metaverse.

4. Networking, including connections related to the metaverse platform as well as supporting sensors and devices. Supporting devices (haptic gloves, body sensors, computational unit, etc.).

Figure 2. Metaverse forensics framework outline

During analysis, malicious or anomalous activities should, optimally, be reported in an automated manner to guide the forensic analysts and speed up investigations. After analysis, any detected signs of compromise, such as cryptocurrency addresses, user activities, or files, should undergo data enrichment. This involves conducting searches across different data sources to find relevant information, which helps provide more detail and context for the analyst.

In the following sections of the blog, we provide a deeper view of how each of the three phases proposed operate, providing the data sources that can be leveraged for each, where applicable.

Triage and artefact collection

Forensic systems can analyze various threat types using multiple data sources. As the fields of forensics and the metaverse develop, the demand for new data sources will grow. It’s important to acknowledge that the available telemetry data can vary based on the platform and hardware in use. The absence of international standards and protocols for the metaverse compounds this complexity. With this in mind, we identify the following data sources as potential telemetry that should be logged to allow the effective analysis of metaverse environments. In addition to the telemetry presented below, forensic triage collection should be performed by capturing the memory and disk image from systems involved in an incident.

Authentication and access data:

◉ User login history, IP addresses, timestamps and successful/failed login attempts.

◉ Session tokens and authentication tokens used for access.

Third-party integration data:

◉ Data from third-party integrations or APIs used in the metaverse platform.

◉ Permissions and authorizations granted to third-party apps.

Error and debug logs:

◉ Logs of software errors, crashes or debugging information.

◉ Error messages, stack traces and core dumps.

Script and code data:

◉ Source code or scripts used within the virtual environment.

◉ Execution logs and debug information.

◉ Smart contracts in relevant blockchain wallets.

Marketplace, commerce data and blockchain:

◉ Records of virtual goods or services bought and sold on the platform’s marketplace.

◉ Payment information, such as credit card transactions or cryptocurrency payments.

User account and user behavior:

◉ Profile username, avatar image, account creation time, account status, blockchain address used to open the metaverse account.

◉ User interactions, friendships, groups, locations, and social networks, while preserving privacy.

◉ User activity logs, including participation in events and in-world gatherings.

User device forensics:

◉ User devices for the extraction of supporting data, such as device activity, configuration files, locally stored chat logs, images, etc.

◉ All ingoing and outgoing network activity reaching devices relevant to a metaverse incident.

Asset provenance data:

◉ Detailed asset provenance information with the complete history of ownership and modifications.

◉ Blockchain addresses and wallets, including a copy of their transaction history. Verification of the “from” address (creator or previous owner) and the “to” address (current owner) is required.

◉ If the asset is digital or represented as a token (e.g., an NFT), examine the smart contract that created it. Smart contracts contain rules and history about the asset.

◉ Ensure the asset is not a copy or fake by verifying that the smart contract and token ID are recognized by the creator or issuing authority.

System and platform configuration:

◉ Details of the platform’s architecture, configurations and version history.

Behavioral biometrics:

◉ Behavioral patterns of user interactions and in-game actions to help identify users based on unique behavior. Although such activity can be useful to identify adversaries in the case where very little is known for their activities, such information is not expected to be widely available.

Telemetry analysis

The goal of the telemetry analysis process is to detect unusual or potentially malicious behavior through a semi- or fully automated processing of data and logs, thereby aiding forensic experts and expediting the investigation process.

This can be accelerated by leveraging deep learning techniques to identify harmful patterns using a database of historically analyzed events. Additionally, incorporating reinforcement learning, refined by forensic experts, could enhance the system’s ability to offer better incident response suggestions. For effective training, these machine-learning algorithms would need access to a large repository of forensic strategies and actions taken by professionals in various investigative scenarios, including those spanning across different metaverse environments and artefacts. Utilizing this data allows the algorithms to match current incidents with similar past cases based on the user input provided.

Given the diverse range of threats and types of incidents, along with the emerging state of the metaverse and its insufficient logging features, devising a comprehensive forensic methodology that is universally applicable to all metaverse platforms or systems presents significant challenges. Should metaverse operators provide telemetry data, the analytical process can be simplified by focusing on artifacts that are most pertinent to a specific incident. Nonetheless, the presence of such artifacts in existing metaverse platforms cannot be assured. To overcome this issue and offer practical guidance, we suggest a hybrid forensic strategy that integrates traditional operating system forensics emphasizing Windows-based platforms due to their prevalent use for client-side metaverse platforms, along with specialized analyses that address the unique aspects of the metaverse and blockchain technologies. For better understanding, we categorize each analytical technique as per the divisions used in the triage and artifact collection section of this blog.

Authentication and access data

Metaverse platforms often store records of successful authentication attempts, including the dates, in local log files. If these logs are unavailable, analyzing DNS records and process executions associated with the metaverse platform can provide insights into when a user accessed it.

One approach to uncover such information involves examining browser records (e.g. Chrome) and the history of visited URLs to identify when a user visited and connected to a specific metaverse platform via a web browser. Additionally, routers may maintain by default traffic logs offering further insight into DNS activity.

For process-related investigation, resources like Amcache and Prefetch are valuable for determining the timing of executions for the metaverse platform client. These tools can help trace the usage patterns and activities associated with user interactions with the metaverse.

Third-party integration data

Acquiring such data can be challenging because these operations occur usually on the backend of servers, and logs related to this activity are typically not accessible to users. To obtain this information, which depends on the architecture and API usage of a metaverse platform, one could use network capture tools like Wireshark. This method allows users to monitor any API requests made while using a metaverse platform, and inspect the contents of these communications, provided they are not encrypted. This approach helps in understanding the interaction between the client and the server during the operation of metaverse platforms.

Error and debug logs

Metaverse platforms commonly record client and connectivity issues in local log files. When these logs are not accessible, one can analyze the Windows Application log to identify any errors issued by the application and any software problems that prevent it from either logging in or functioning properly. However, it is important to note that errors occurring specifically within the metaverse environment are not captured by Windows’ native logs, thus remaining invisible to analysts using these tools.

Script and code data

In certain environments, snippets of scripts and other code that serve various functionalities can be accessed through reverse engineering, allowing analysts to determine if a metaverse feature is functioning properly and safely. However, it’s important to note that reverse engineering software may be illegal and is generally advised against.

Despite these limitations in directly analyzing metaverse code, it is still feasible to examine publicly available smart contract code. This code governs on-chain transactions and facilitates exchanges of value between players in metaverse environments. To analyze the smart contract associated with a specific metaverse, one must first identify the blockchain it utilizes. Then, by finding the smart contract’s address, one can inspect its code using a blockchain explorer. For instance, to review the smart contract of UNI (a decentralized exchange) which operates on the Ethereum blockchain, one would use an Ethereum blockchain explorer to locate and examine the contract’s code at the Ethereum address (0x1f9840a85d5aF5bf1D1762F925BDADdC4201F984) used by UNI.

Marketplace, commerce data and blockchain

Transaction records of virtual goods or services exchanged on a metaverse platform can be tracked by examining a user’s account to review the NFTs and other items they possess. Additionally, by conducting on-chain transaction analysis, one can retrieve a complete history of item ownership, including details of items or NFTs bought and sold by users. Thanks to the transparency of public blockchains, this process is straightforward. It only requires the wallet address used by the user to access the metaverse platform. This address can be searched in the relevant blockchain explorer to analyze the user’s historical transactions and items purchased or sold.

User accounts and behavior

Currently, the logging and analytics of user behavior within metaverse environments are largely undeveloped. Basic information like profile usernames and avatar images are stored locally in the metaverse client’s directory. More detailed information about user interactions, friendships, groups, and visited locations can be retrieved from a user’s account, provided the data has not been deleted by the user. Analyzing a user’s social networks may offer deeper insights into their participation in metaverse events and related in-world gatherings.

User device forensics

Various devices enable interaction with the metaverse, including VR headsets, smartphones, gaming consoles and haptic gloves. The extent of data logging varies by device. For example, VR headsets may record details such as connected social networks, usernames, profile pictures and chat logs. It is essential to analyze the specific vendor and device to determine the availability of such logs. As the technology landscape evolves, it is anticipated that more vendors and devices will emerge, further complicating the environment. This dynamic nature will necessitate more sophisticated tools and greater expertise for effective forensic analysis in the future.

Asset provenance data

Detailed information about the provenance of assets in the metaverse, including the complete history of ownership and modifications, can be obtained through on-chain analysis. This process involves examining transactions between blockchain addresses of interest, the non-fungible tokens (NFTs) and other tokens they possess, and their interactions with smart contracts. Because public blockchains are immutable — meaning that once data is recorded, it cannot be deleted or changed — it is relatively straightforward to track asset provenance. By searching for a known wallet address in the appropriate blockchain explorer, one can easily trace the history associated with that address.

When analyzing blockchain data for provenance, it is critical to verify that the addresses interacting with the target address are legitimate. This includes ensuring that entities like metaverse providers or NFT issuers are not misrepresented by posing as the official addresses. Verification can be achieved by visiting the official website of the token or metaverse provider to find and confirm their official blockchain addresses. This step is crucial to ensure that the address in question belongs to the entity it claims to represent. An illustrative case would be investigating the purchase of an expensive plot in the metaverse. Suppose an analysis of a user’s blockchain address reveals an NFT transaction from another address, which purportedly represents a plot identical to the one purchased. However, the source address sending the NFT is not the official one used by the metaverse provider for NFTs. If this discrepancy goes unchecked, it could obscure potential fraud or suspicious activities.

Another key factor in asset provenance is linking blockchain addresses to actual user identities. While blockchain technology typically provides pseudonymity, there are services that offer extensive databases capable of associating specific addresses with various entities and exchanges. This capability enhances an investigator’s ability to trace asset flows more effectively. For instance, WalletExplorer is a website that provides free services for attributing addresses on the Bitcoin network.

System and platform configuration

To effectively investigate a metaverse platform, it’s essential to gather detailed information about its system, architecture, and configuration. However, obtaining this information can be challenging as it is often limited. When available, key sources include official websites, developer documentation, user forums, and community pages. Additionally, valuable insights into the platform’s configuration can often be gleaned from debug and error logs, where these are accessible.

Behavioral biometrics

Behavioral patterns, such as user interactions and in-game actions, are key in identifying users based on their unique behaviors and detecting potential account hijacks. These behaviors can include movement and gesture recognition, voice recognition and the patterns of typing and communication. Additional metrics may involve how users interact with in-game items and other participants.

Currently, most systems used to interact with the metaverse do not extensively log such information, which limits the capacity for in-depth behavioral analysis. What is typically available for analysis includes communication patterns derived from chat logs and basic interaction patterns. These interactions are often analyzed through chats, the groups users join, events they attend, and on-chain analytics for transactions and engagements within the virtual space. This level of analysis, while helpful, only scratches the surface of what could potentially be achieved with more comprehensive behavioral data collection and analysis.

Data enrichment

Following analysis, it is crucial to correlate and analyze diverse data types from multiple sources, including blockchain transactions, IPFS storage, internet-of-things (IoT) devices and activities within the metaverse. Drawing from research, a forensic framework could use APIs from diverse data repositories to aggregate pertinent information. Such information can be retrieved from blockchain analytics vendors for the identification of malicious wallet addresses or traditional databases containing threat intelligence for malicious IP addresses and file hashes. The gathered data can then be processed through Named Entity Recognition (NER) to cleanse the data to extract relevant information and diminish data clutter in larger datasets, ensuring analysts receive concise and clear insights. Enriching threat intelligence demands considerably more effort beyond conventional practices, extending beyond mere checks of IPs, URLs, file hashes and online adversarial behavior. It also encompasses the analysis of blockchain transactions, provenance of digital assets, and the scrutiny of entities within the metaverse, such as casinos and conference venues, given that logs are available for analysis.

The insights gained from each case should be meticulously documented in public databases, outlining the tactics, techniques and procedure employed by adversaries within the metaverse. This documentation aids in refining the forensic capabilities of metaverse systems and provides forensic examinators intelligence for more effective and precise attributions. The selection of data sources for threat intelligence augmentation can be tailored based on investigative needs and emerging developments in the field. While it’s crucial to continue employing conventional threat intelligence strategies to address more traditional and legacy aspects of investigations, for metaverse-specific inquiries, relevant data sources might include:

• The source code of blockchains or smart contracts (e.g., from GitHub).
• IPFS (Interplanetary File System) frameworks.
• Blockchain analytics tools.
• Social media and community monitoring for discussions and trends on social media.

Source: cisco.com

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Demonstrating Trust and Transparency in Mergers and Acquisitions

Demonstrating Trust and Transparency in Mergers and Acquisitions 

All good relationships are built on trust. Add in transparency, and the union becomes even more substantial. “Trust and transparency underpin everything we do,” says Button, “Cisco takes security, trust, and transparency very seriously, and it’s part of our team’s fabric.”

When Cisco acquires a company, the Security and Trust M&A team looks at not only what they can offer in the way of security but also what unique qualities the acquired company brings to Cisco. These qualities might be related to security, but they’re also found in the acquired company’s culture, technical knowledge, and processes.

In all acquisitions, the M&A team needs to move fast. In fact, the Cisco team is committed to pushing even faster as long as they never compromise on security. Around 2020, Button and his team began taking stock of how it does things. They evaluated everything from the ground up, willing to tease out what is working and toss out what isn’t.

The team is also on a trajectory of identifying how it can digitize and automate security.

“If we were going to do things differently, we needed to be bold about it,” says Mohammad Iqbal, information security architect in the Security and Trust M&A team. One of the changes Iqbal proposed to his colleagues is to ensure that an acquired company is integrated into Cisco’s critical security controls within three months after the acquisition deal closes.

Focus on Non-Integrated Risks

To successfully meet the three-month target, the M&A team works closely with the acquired company to identify and address all non-integrated risks (NIRs) that Cisco inherits from an acquisition and encompass:

◉ Visibility to get the acquired company integrated into the governance process; includes risk assessments and familiarity with all the players involved in the acquisition

◉ Vulnerability management to identify and remediate vulnerabilities. Where do the acquisition’s crown jewels reside? What does the external attack surface look like? Has it been patched?

◉ Security operations to determine such functions as identity, administrative access, multifactor authentication, and basic monitoring.

NIRs are a subset of eight security domains, or operating norms, that align with Cisco’s security and trust objectives and top priorities of the larger security community (Figure 1). The M&A team’s focus on NIRs steers the due diligence conversation away from identifying the acquisition’s security deficiencies and towards understanding the inherent risks associated with the acquisition and measuring the security liability.

“Acquisitions are coming in with these risks, and so we must address NIRs early when we’re signing non-disclosure agreements. In doing so, we help put these companies in a position to integrate successfully with all the security domains. And this integration should be done in the shortest time possible within a year of close,” Iqbal says.

Figure 1. Cisco’s Eight Security Domains

Building trust and being transparent early on is critical so the acquired company knows what’s expected of them and is ready to accomplish its three-month and first-year goals.

“I wish this type of conversation was offered to me when Cisco acquired Duo,” Button says. “Being on the Duo side of that deal, I would’ve been able to say with confidence, ‘OK, I get it. I know what’s expected of me. I know where to go. I know what I need to do with my team.’”

“We have a limited time window to make sure an acquisition company is heading down the right route. We want to get in there early and quickly and make it easy,” adds Button.

Time Is of the Essence

Reducing the manual intervention required by the acquired company is integral to helping the acquisition meet the three-month goal. Here’s where automation can play a significant role and the M&A team is looking toward innovation.

“We’re working on bringing in automated processes to lessen the burden on the acquired company,” says Iqbal. The M&A team realizes that much of the automation can be applied in instrumenting the security controls and associated APIs to help the team move beyond what they have already assessed at acquisition day 0 and gain the visibility they need to get the acquired company to its three-month goal. For example, they can automate getting the acquired company on Cisco’s vulnerability scans, using internal tools, or attaining administrative access privileges.

So, Iqbal, Button, and the rest of the team are working on automating processes—developing the appropriate architecture pipeline and workflows—that help acquired companies integrate critical security controls. While the ability to automate integration with security controls is not novel, the innovation that the M&A team brings to the table is the ability to position an acquired target to integrate with security controls in the most expedited way possible.

Automation in Discovery

As with due diligence, the M&A team strives to complete the discovery phase before the acquisition deal close. Here’s another step where digitization and automation can simplify and shorten processes. Take the acquisition company questionnaire, for instance.

“Instead of asking dozens of questions, we could give the company an audit script to run in their environment,” Iqbal says. “Then, all they have to do is give us the results.”

Also, the questionnaire can be dynamically rendered through a dashboard, improving the user experience, and shortening completion time. For example, the number of questions about containers could automatically retract if the acquired company uses Azure Kubernetes Service.

After the Close

Many teams within Cisco compete for an acquired company’s time before and after an acquisition deal closes. The acquired company is pulled in several different directions. That’s why the Security and Trust M&A team doesn’t stop looking for ways to digitize and automate security processes after the close—to continue to help make the acquired company’s transition more manageable.

“If we can make processes simple, people will use them and see the value in them within days, not weeks or quarters,” says Button.

“The majority of companies we acquire are smaller,” Button says. “They don’t have large security teams. We want them to tap our plethora of security experts. We want to enable an acquired company to apply Cisco’s ability to scale security at their company. Again, we want things to be simple for them.”

The M&A team helps facilitate simplicity by telling a consistent story (maintaining consistent messaging unique to the acquired company) to all the groups at Cisco involved in the acquisition, including M&A’s extended Security and Trust partners such as corporate security, IT, and supply chain. Because each group deals with different security aspects of the integration plan, it’s essential that everyone is on the same page and understands the changes, improvements, and benefits of the acquisition that are relevant to them. Maintaining a consistent message can go a long way toward reducing complexity.

It’s All About Balance

The human element can easily get overlooked throughout an acquisition’s myriad business, technical, and administrative facets. Balancing the human aspect with business goals and priorities is essential to Button and the entire Security and Trust M&A team. They want to bring the human connection to the table. In this way, trust and transparency are on their side.

“Emotions can run the gamut in an acquisition. Some people will be happy. Others will be scared. If you don’t make a human connection, you’ll lose so much value in the acquisition,” Button says. “You can lose people, skillsets, efforts. If we don’t make that human connection, then we lose that balance, and we won’t be off to a great start.”

One way the M&A team helps maintain that balance is by embracing the things that make the acquired company unique. “It’s vital to identify those things early on so we can protect and nurture them,” says Button.

He also wants to remind companies that they don’t have to be experts at everything asked of them during acquisition. “Cisco has been here for a while. We have entire teams within M&A that are dedicated to doing one thing. We can help acquired companies find out where they’re struggling. We can handle the things they don’t want to deal with.”

“M&A is complex, but complexity is off the chart when you talk about M&A and security. Our team won’t be successful if we can’t find a way to make things easier for the acquired company. They need to understand where they’re headed and why,” Button says. “It’s up to us to motivate them towards a successful outcome.”

Source: cisco.com

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FFIEC Cybersecurity Maturity Assessment Tool

Financial institutions have to be vigilant in the face of a continually evolving cybersecurity threat landscape. As these have attacks have evolved, regulatory bodies have updated their regulations to account for the increasing threat of cyber risk. In 2015, following a significant increase in nation state and hacktivist attacks on U.S. financial institutions, the FFIEC released new guidance and a Cybersecurity Assessment Tool for institutions to self assess their risks and determine their cybersecurity maturity. This was revised in 2017, and this consistent framework is intended to be able to help leadership and the board assess their preparedness and risk over time. This framework is especially relevant given the recent FFIEC Architecture and Operations update and the Executive Order on Cybersecurity from 2021.

The purpose of this blog is to assist our IT based customers and partners with a concise and high level understanding of the FFIEC Cybersecurity Assessment Tool and derivative impacts on their current and future day to day operations. It is part of a multipart blog series on financial regulations and how to manage them architecturally, geared towards IT leadership.

The Cybersecurity Assessment Tool is fairly intuitive to use and the exercise should not be arduous for an organization to complete. The assessment applies principles of the FFIEC IT Handbook and the NIST Cybersecurity Framework. The intention here was to be complimentary to existing frameworks and supportive of existing audit criteria. The FFIEC has released a mapping of the Cybersecurity Assessment Tool and the NIST Cybersecurity Framework to the FFIEC IT Handbook.

How the Assessment works:

The assessment itself involves two primary components: an institution first creates an inherent risk profile based upon the nature of their business, and determining cybersecurity maturity. The inherent risk profile is an institution’s analysis of its key technologies and operations. These are mapped into categories and include:

1. Technologies and Connection Types

2. Delivery Channels

3. Online Mobile Products and Technology Services

4. Organizational Characteristics

5. External Threats

The tool itself provides guidance on criteria to sell assess risk based on the different characteristics of an organization, which simplifies completion as well as consistency. By having explicit guidance on how to self assess into different risk categories, the leadership for the institution can ensure they have a consistent understanding of what the risk entails.

Below is a snippet of the inherent risk profile, of note is the intuitive and consistent guidance on how to classify risk within each domain.

The second aspect of the assessment is understanding cybersecurity maturity. This section can help leadership understand the risk and appropriate controls which have been put into place. It creates five levels of maturity, from baseline to innovative, and we use these to measure preparedness of the processes and controls for five risk domains:

1. Cyber Risk Management and Oversight

2. Threat Intelligence and Collaboration

3. Cybersecurity Controls

4. External Dependency Management

5. Cyber Incident Management and resilience.

The five domains include assessment factors and declarative statements to help management measure their level of controls in place. What this means is there are statements within each assessment factor that describe a state. If those descriptive statements matches a financial systems controls, then they can claim that level of cybersecurity maturity. Of important note however, as in the picture above, the levels are additive, like a hierarchy of needs. What this means is that if there is a statement in innovative that matches some of your organizations controls, but you haven’t satisfied the statements in the “advanced” guidance, you can not measure your institution as innovative in that domain. Likewise, an intermediate level of maturity assumes that all criteria in the evolving level, have been met.

The five domains each have various assessment factors. For example, in cybersecurity controls there are assessment factors for preventative, detective, and also corrective controls. Each of these assessment factors will have contributing components which are then measured. An example of this is within the preventative controls assessment factor, there is components such as “infrastructure management” and “access and data management”.

It becomes easier to envision when evaluating the assessment document and the corresponding components. As can be seen in the below cybersecurity guidance, there are a number of explicit statements that describe maturity at a particular level and mapping to regulatory requirements. Through satisfying these statements you can appropriately match your institution to its level of cybersecurity maturity.

The Next Step

Following completion of an inherent risk profile and cybersecurity maturity an organization can determine if they have the appropriate controls in place to address their inherent risk. As inherent risk increases, obviously a higher level of security controls should be positioned to provide a level of control around that risk. A conceptual guidance on how risk should map to maturity is outlined below. Where this becomes important is not only in determining a point in time deficiency, but understanding that as new projects, acquisitions, or the threat environment changes, leadership can understand whether increases in security controls need to be applied to adequately address a material change in risk level.

Derivative Impacts on Infrastructure and Security Teams

The Cybersecurity Assessment is a useful tool for financial institutions to consistently provide leadership a synopsis of the state of the institution. But how this translates downstream to day to day operations of architects may not be explicit. There are a number of areas in the Cybersecurity Maturity section where explicit guidance is given which we have seen undertaken as projects at our customers, as well as across the industry. Below are a few themes we have seen gain in prominence since the publishing of the assessment. These weren’t generated by the assessment itself, but are common themes across the industry. Through this blog, the intent is more to provide a high level synopsis of how these projects influence, and are influenced by, and measured through, the regulatory bodies.

1. Segmentation is explicitly called out with guidance given on how to measure. We have seen this translated across the industry as both Macro and Micro segmentation approaches, and both of these are complimentary. These have driven technologies such as SD-Wan, SD-Access, ACI, and VXLan based segmentation.

2. Managing infrastructure and lifecycle hardware and software versions are measured. This practice isn’t specific to just this assessment and it has become a common theme to be able to keep devices in patch management. It is a shift from some institutions “sweating their assets” to a proactive model for managing. What had been observed was “hackers love sweaty assets”, with most exploits targeting known vulnerabilities. This should translate into any new technology investment having a lifecycle that can ensure the full depreciation of the asset while maintaining patch management.

3. Analytics and telemetry have driven significant investments in cybersecurity operations team’s ability to understand and act upon emerging threats in real time. Leveraging existing assets as sensors or sources of meaningful telemetry is important as deploying dedicated appliances to the larger attack surfaces of campuses, branches, and wireless  nd can be prohibitively expensive plus operationally unsupportable.

The above is just a few of the many derivative impacts that affect our infrastructure and security teams. With increasing nation state guidance on security and privacy, to include the U.S. Executive order on Cybersecurity, additional tightening of conformance to address evolving security risks is happening. A lot of the increased focus aligns to areas which occur within existing domains that are included in existing frameworks. The FFIEC Cybersecurity Maturity Assessment is a simplified tool that can help a board member understand which security controls should be addressed first.

Source: cisco.com

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How Cybersecurity Leads to Improved Sustainability

After managing the sudden switch to remote work in 2020, organizations are making a more permanent transition into the flexible hybrid workforce. The Federal Bureau of Investigation (FBI) found that cybersecurity attacks rose by 3-4 times from the transition to remote work in 2020. In addition, experts predict that ransomware will cost the world up to $20 billion in 2021 and is expected to be a greater concern with the hybrid work model. As a result, you’ll need to rapidly scale your security to account for the massive influx of remote and hybrid workers while simplifying and unifying your IT systems.

While implementing security controls is increasingly important, this also means more hardware appliances and virtual instances to secure different parts of the infrastructure. All this extra equipment and instances means more power consumption and heat dissipation, leading to adverse impacts on the environment. We’re taking steps to address this situation. There are a couple of ways we’re approaching this. Cisco products have security features which are built into our switches to prevent the need for separate security appliances.

Innovative methods to detect malware within encrypted layers

As an example, let’s look at the scenario where a traditional method of securing the deployment is used for decryption and identification of malware. As shown in Figure 1, you would first need to decrypt the traffic, then apply analysis (inspection / anti-malware), and finally encrypt the traffic again. The resulting power consumption is shown in Table 1.

Figure 1. Traditional deployment using Secure Sockets Layer (SSL) inspection

Table 1. Power consumption in a traditional deployment

As displayed in Table 1, the total power consumption for all the devices is close to 9500W. In the sustainable method we offer, the Cisco Secure Network Analytics (Cisco Stealthwatch) components like Stealthwatch Management Console (SMC) and Flow Collector (FC) are virtualized, which can be deployed on the existing X86 servers without needing the additional devices as shown in Figure 2.

Figure 2. Innovative and sustainable option using Cisco Secure Network Analytics (Stealthwatch)

In this scenario, Stealthwatch’s patented technology allows analysis of encrypted traffic without decryption. The ETA module in the catalyst switch provides Stealthwatch with the extra information for the analysis of the encrypted traffic without decryption.

Table 2. Power consumption using Cisco Secure Network Analytics with Catalyst switches

As the Stealthwatch components are virtual, they can be deployed in an existing X86 server, and the power consumption is minimal as compared to the dedicated appliances.

Another way Cisco caters to sustainable cybersecurity is by ensuring that the functionalities such as load balancing, packet broker functions, switching, and routing are all included in a single appliance.

Tables 3-4 highlight the difference between the traditional method and innovative new method for total power consumed for identifying malware in encrypted traffic:

Table 3. Traditional method power consumption

All the functionalities listed in Table 3 are now available in a single switch such as the Nexus NX 9300, which has the following power consumption:

Table 4. Power consumption using Cisco Nexus

This shows that there are alternate methods to detect malware within encrypted layers which are more sustainable, efficient, and less expensive compared to traditional deployments.

Source: cisco.com

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