MGM Resorts shuts down IT systems after cyberattack
Introduction
MGM Resorts, which is an international company, has suffered an ongoing cyberattack which led to the shutdown of a number of its computer systems, including its website, in response to a cybersecurity issue. MGM Resorts International is in touch with external cybersecurity experts to resolve the issue since it has affected its entire Computer systems. MGM is a larger entity and operates thousands of hotel rooms across Las Vegas and the United States. MGM Resorts shared about the incident and posted that MGM recently identified a cybersecurity issue affecting some of the Company's systems. Promptly after detecting the issue, they quickly began an investigation with assistance from leading external cybersecurity experts. MGM has notified law enforcement and took prompt action to protect systems and data, including putting down certain systems. MGM further stated that the investigation is ongoing.
The issue
Basic operations such as the online reservation and booking system MGM have been affected and shut down due to the cybersecurity issue faced by a lot of visitors. Since earlier times, casino security has been the state of the art as they were very vulnerable to attacks by robbers and con artists. This is what we have also seen in a lot of movies. In today's time, con artists and robbers are now strengthened by cyber tactics. This is exactly what was seen in the case of the MGM attack.
MGM Resorts is home to best-in-class amenities and facilities for guests, but with the increase in tourist traction, the vulnerabilities and the scope of cyber attacks have also increased. This is also because of open wifis in the establishments and the transition of casinos to e-casinos, thus causing a major shift towards digital and technology-based intervention for better customer experience and streamlining a lot of operations.
How real is the threat?
As reported by MGM Resorts, the following systems were impacted in the cyber security attack:
- Slots Machines: The slot machines placed in the casino suddenly went offline and displayed an error message for the players. Some players who were already using the slot machines lost their bets and were unable to withdraw their winnings.
- Room Keys: Some of the guests reported that the room keys became unresponsive, and in some cases, the replacement keys were also inactive for some time, causing massive chaos at the reception.
- Booking Status: All the bookings in today's time are made online; this was one of the worst-hit segments of the cyber attacks. Most of the bookings made automatically were put on hold, and the confirmations could be made only from the hotel reception, thus causing massive cancelling of the bookings and both the hotel and customers losing out on money.
- MGM App: The official app of MGM Resorts was completely down, thus causing a situation of confusion and panic among the guests. The users also received notifications to speak to different customer care executives, but some of the numbers were unattentive and seemed to be operated by bad actors.
- Data breach: The main focus of the cyber attack was dedicated to committing a data breach. The attack led to the breach of personal data of most of the users registered on the app or on the system of MGM Resorts.
Conclusion
The cyber attack on the tourism industry is a major and growing concern for the industry and its customers. Seeing the volatility of the data and the regular inflow of personal information this makes the hotel's cyber security system a vulnerable choice for bad actors. The cyber attack was no less than a fire sale, where in all the segments of the services offered were impacted. Similar attacks were reported by MGM in 2019 and 2020, and subsequently, the safety measures were also deployed, but the bad actors have hit the resorts chain owners again, in such cases the most paramount defence is having a safe and regularly updated firewall, upskilling of staff for IT issues and attacks, active reporting and investigation mechanisms for assisting the LEAs. In the times of rising cyberattacks, one needs to be critical of their data management and digital footprints. The sooner we adopt safe, secure and resilient cyber hygiene practices, the safer our future will be.
References:
https://www.bleepingcomputer.com/news/security/mgm-resorts-shuts-down-it-systems-after-cyberattack/
https://www.cnbc.com/2023/09/12/mgm-resorts-cybersecurity-incident-forces-system-outage.html
Related Blogs
The Expanding Governance Challenge of Artificial Intelligence
Artificial intelligence (AI) systems are increasingly embedded in economic and social infrastructure. They are being adopted in financial services, healthcare diagnostics, hiring systems, and public administration. But while these systems improve efficiency and decision-making, they also introduce new forms of technological risk.
Unlike conventional software, AI systems learn patterns from data and continue to evolve as they run. This poses governance issues since risks can arise throughout the AI life cycle, whether at the coding level or in their implementation.
The latest regulatory frameworks, such as the European Union’s AI Act (EU AI Act) and the UNESCO Recommendation on the Ethics of Artificial Intelligence, note that responsible AI governance depends on the realisation of where risks emerge across the development process.
This article maps the AI system lifecycle, identifies the risks that emerge at each stage and evaluates the policy tools used to mitigate them using the lifecycle framework developed by the Organisation of Economic Co-operation and Development (OECD).
The Lifecycle of an AI System
AI systems are developed through a structured process that includes problem definition, dataset collection and preparation, model development, testing and validation, deployment, and monitoring.
The OECD conceptualises this development process as the AI system lifecycle. Each stage entails various technical and administrative procedures, since choices made during these stages will dictate the goals and limits of an AI system. Further, the quality and representativeness of training sets will have a strong effect on the behaviour of models after implementation.
Since this is an iterative and not a linear procedure, risks can be introduced at each stage of the AI lifecycle. New data can be retrained into different models, and systems are regularly updated once they have been deployed, to address performance degradation, model errors, or unintended outputs. This iterative process means governance must address risks across the entire lifecycle, not just at deployment.
Where AI Risks Emerge
AI risks usually emerge earlier in the development process, especially in the phases when system objectives are formulated and training data are chosen. The EU AI Act and the UNESCO Recommendation on the Ethics of AI outline the following risks: bias and discrimination, privacy and data security violations, the absence of transparency in automated decision-making, and risks to fundamental rights.
AI Governance Risk Landscape: Core Risk Categories Under International Frameworks
Risk categories jointly identified by the EU AI Act and UNESCO Recommendation on the Ethics of Artificial Intelligence
Outlining the risks throughout the AI lifecycle helps understand the areas where governance interventions are most necessary. For example, discriminatory outcomes often result from biased or unrepresentative training data, while safety failures are typically linked to inadequate testing before deployment. Risks such as misinformation arise post the development process, when generative AI systems are deployed at scale on digital platforms.
AI System Lifecycle: Key Risks at Each Stage
Risks identified per the EU AI Act and UNESCO Recommendation on the Ethics of AI
Understanding where risks emerge across the lifecycle explains why governance frameworks classify AI systems by risk and apply oversight at multiple stages.
Policy Tools for Mitigating AI Risks
Governments and international organisations have developed regulatory tools to help mitigate AI risks in the lifecycle. These tools are meant to make sure that AI technologies are identified as up to standard in safety, accountability and fairness prior to and after deployment.
For example, the OECD AI Policy Observatory recommends that governments adopt policy instruments such as risk evaluations, algorithmic auditing necessities, regulatory sandboxes, and transparency necessities of AI systems. The European Union’s Artificial Intelligence Act (AI Act) is one of the most comprehensive systems of governance that introduces a risk-oriented regulation strategy. It mandates adherence to requirements concerning data governance, documentation, human oversight, and robustness, and cybersecurity. Such requirements bring regulatory checkpoints to the lifecycle of AI systems.
Mapping these policy tools across the lifecycle illustrates how governance mechanisms can intervene at different stages of AI development.
Governance Overlay: Policy Interventions Across the AI Lifecycle
Regulatory tools mapped at each stage of AI development per the EU AI Act and UNESCO Recommendation on the Ethics of AI
Several policy tools are directed at the risks that occur in the pre-developmental stages. In one example, algorithmic impact assessment has been applied in various jurisdictions to measure the possible consequences of automated decision systems on society before implementation. On the same note, the requirements of dataset documentation, including dataset transparency requirements and model cards, are aimed at enhancing accountability during the training and development stages of the AI systems. Therefore, lifecycle-based policy design allows regulators to intervene before harmful outcomes occur, rather than responding only after AI systems have caused damage in real-world environments.
The Policy Gap in AI Governance
The misalignment between risks and governance tools across the AI lifecycle indicates a critical structural gap in existing regulations. Numerous governance processes become activated after AI systems are classified as “high risk” or after they are implemented in the real world. But the most serious sources of damage have their roots in earlier stages of the development procedure.
An example is that prejudiced or unbalanced training data is almost inevitably a source of discriminative results in automated decision systems. When these types of models are applied in areas like staffing, credit rating, or in providing services to the public, such biases can quickly spread to large populations and undermine democratic rights. In the same way, the lack of transparency in model design might result in the fact that the regulator or individuals are affected by the decision-making process. This reflects a broader timing gap in AI governance, where risks originate during design and development, but regulatory intervention typically occurs only after deployment.
Analysis
1. Key risks originate before deployment: As depicted in the lifecycle mapping, the data collection and model development phase presents several significant governance risks as opposed to the deployment phase. Structural issues can be entrenched within AI systems even before they are deployed in practice due to bias in data sets, incomplete reporting of training sets, and obscured network designs.
2. Data governance is a primary point of vulnerability: Most of the instances of algorithmic discrimination listed above are associated with training material that is not representative of some population groups or is historical. Since machine learning models are optimisations of patterns that exist in datasets, these biases can be carried through the whole lifecycle and reproduced after deployment.
3. Regulatory approaches remain mismatched across jurisdictions: Different countries adopt varying approaches to AI governance, ranging from risk-based frameworks such as the EU AI Act to more sector-specific or voluntary guidelines in other regions. This divergence creates inconsistencies in safety, accountability, and enforcement standards, allowing risks to persist across borders and potentially undermining the protection of users in globally deployed AI systems.
4. Governance interventions remain uneven across the lifecycle: Whereas the various regulatory instruments aim at deployment and monitoring, fewer instruments systematically tackle the risks that are posed by the previous design and development phases.
Recommendations
1. Introduce mandatory lifecycle risk assessments: The regulatory systems need to demand systemic risk evaluation at the beginning of AI development, especially at the problem design and dataset selection phases. This would assist in detecting possible harmful applications in advance, before systems are constructed and installed.
2. Strengthen dataset governance standards: Training datasets must be supplemented with documentation as to their provenance, composition and limitations. Standardised documentation frameworks of data sets can assist in the discovery by regulators and auditors of the potential sources of bias or privacy threats.
3. Expand independent algorithmic auditing: AI systems can be assessed by regular third-party audits based on fairness, strength, and security weaknesses. The auditing mechanisms especially apply to high-risk systems employed in employment, finance or the public services.
4. Integrate continuous monitoring requirements: AI systems may be monitored regularly after implementation to identify model drift, unforeseen consequences, or abuse. Reporting systems can facilitate the process where the regulators can see the emerging risks and modify the governance systems.
Conclusion - The Need for Global AI Governance
Despite growing regulatory attention, global air governance remains fragmented. Different jurisdictions adopt varying approaches to risk classification, oversight, and enforcement, leading to inconsistencies in safety and accountability standards. Given that AI systems are often developed, deployed, and used across borders, this lack of coordination allows risks to persist beyond national regulatory frameworks.
Addressing these challenges requires a shift towards greater international cooperation and lifecycle-based governance. Developing shared standards, improving cross-border regulatory alignment, and embedding oversight across all stages of AI development will be essential to ensuring that AI systems are safe, transparent, and accountable in a globally interconnected environment.
References
- OECD AI lifecycle
- OECD AI system lifecycle description
- OECD AI governance lifecycle framework
- EU AI Act overview
- EU AI Act risk categories
- UNESCO Recommendation on the Ethics of AI
- AI governance lifecycle analysis
- OECD AI policy tools database
Executive Summary
A claim circulating on social media alleges that India refused to unload crude oil from two Iranian tankers following a call between US President Donald Trump and Prime Minister Narendra Modi, after the US announced fresh restrictions on Iranian oil exports. However, research by the CyberPeace Research Wing found the claim to be misleading. The probe revealed that two supertankers carrying Iranian crude are currently anchored off India’s western and eastern coasts. No credible evidence or reports suggest that India refused to unload the cargo or sent the vessels back.
Claim
A user on X claimed that India returned 2 million barrels of Iranian crude oil after a phone call from Donald Trump. According to the post, India had already paid for the oil and the tanker was en route, but following the call with Narendra Modi, authorities refused to unload the shipment and sent the tanker back to Iran.
Fact Check
No credible national or international media reports were found to support the claim that India refused to accept Iranian oil or returned the tankers. Given the global scrutiny on oil shipments amid tensions in West Asia, any such development would have drawn widespread coverage. According to Reuters, two large crude carriers loaded with Iranian oil reached Indian ports on April 13. The Iran-flagged Felicity arrived near Sikka port in Gujarat, while the Curacao-flagged Jaya reached Paradip port in Odisha. The report noted that this marked the first purchase of Iranian oil by Indian refiners since 2019.
Further, The Times of India reported that Felicity, owned by the National Iranian Tanker Company, anchored off Sikka on April 12 carrying around 2 million barrels of crude loaded from Kharg Island in mid-March. The second tanker, Jaya, also anchored near Paradip around the same time, having departed with a similar volume of crude in late February. While the buyers of these cargoes have not been officially disclosed, Paradip port is primarily used by Indian Oil Corporation, while Sikka port is used by Reliance Industries and Bharat Petroleum Corporation.
Conclusion
The viral claim is false and misleading. Available evidence shows that the Iranian oil tankers are stationed near Indian ports, and there is no confirmation that India refused to unload the cargo or sent the vessels back.
Introduction
Governments worldwide are enacting cybersecurity laws to enhance resilience and secure cyberspace against growing threats like data breaches, cyber espionage, and state-sponsored attacks in the digital landscape. As a response, the EU Council has been working on adopting new laws and regulations under its EU Cybersecurity Package- a framework to enhance cybersecurity capacities across the EU to protect critical infrastructure, businesses, and citizens. Recently, the Cyber Solidarity Act was adopted by the Council, which aims to improve coordination among EU member states for increased cyber resilience. Since regulations in the EU play a significant role in shaping the global regulatory environment, it is important to keep an eye on such developments.
Overview of the Cyber Solidarity Act
The Act sets up a European Cyber Security Alert System consisting of Cross-Border Cyber Hubs across Europe to collect intelligence and act on cyber threats by leveraging emerging technology such as Artificial Intelligence (AI) and advanced data analytics to share warnings on cyber threats with other cyber data centres across the national borders of the EU. This is expected to assist authorities in responding to cyber threats and incidents more quickly and effectively.
Further, it provides for the creation of a new Cybersecurity Emergency Mechanism to enhance incident response systems in the EU. This will include testing the vulnerabilities in critical sectors like transport, energy, healthcare, finance, etc., and creating a reserve of private parties to provide mutual technical assistance for incident response requests from EU member-states or associated third countries of the Digital Europe Programme in case of a large-scale incident.
Finally, it also provides for the establishment of a European Cybersecurity Incident Review Mechanism to monitor the impact of the measures under this law.
Key Themes
- Greater Integration: The success of this Act depends on the quality of cooperation and interoperability between various governmental stakeholders across defence, diplomacy, etc. with regard to data formats, taxonomy, data handling and data analytics tools. For example, Cross-Border Cyber Hubs are mandated to take the interoperability guidelines set by the European Union Agency for Cybersecurity (ENISA) as a starting point for information-sharing principles with each other.
- Public-Private Collaboration: The Act provides a framework to govern relationships between stakeholders such as the public sector, the private sector, academia, civil society and the media, identifying that public-private collaboration is crucial for strengthing EUs cyber resilience. In this regard, National Cyber Hubs are proposed to carry out the strengthening of information sharing between public and private entities.
- Centralized Regulation: The Act aims to strengthen all of the EU's cyber solidarity by outlining dedicated infrastructure for improved coordination and intelligence-sharing regarding cyber events among member states. Equal matching contribution for procuring the tools, infrastructure and services is to be made by each selected member state and the European Cybersecurity Competence Centre, a body tasked with funding cybersecurity projects in the EU.
- Setting a Global Standard: The underlying rationale behind strengthening cybersecurity in the EU is not just to protect EU citizens from cyber-threats to their fundamental rights but also to drive norms for world-class standards for cybersecurity for essential and critical services, an initiative several countries rely on.
Conclusion
In the current digital landscape, governments, businesses, critical sectors and people are increasingly interconnected through information and network connection systems and are using emerging technologies like AI, exposing them to multidimensional vulnerabilities in cyberspace. The EU in this regard continues to be a leader in setting standards for the safety of participants in the digital arena through regulations regarding cybersecurity. The Cyber Solidarity Act’s design including cross-border cooperation, public-private collaboration, and proactive incident-monitoring and response sets a precedent for a unified approach to cybersecurity. As the EU’s Cybersecurity Package continues to evolve, it will play a crucial role in ensuring a secure and resilient digital future for all.
Sources
- https://www.consilium.europa.eu/en/press/press-releases/2024/12/02/cybersecurity-package-council-adopts-new-laws-to-strengthen-cybersecurity-capacities-in-the-eu/
- https://data.consilium.europa.eu/doc/document/PE-94-2024-INIT/en/pdf
- https://digital-strategy.ec.europa.eu/en/policies/cybersecurity-strategy
- https://www.weforum.org/stories/2024/10/cybersecurity-regulation-changes-nis2-eu-2024/
