By Adam Clark

This briefing provides an overview of cybersecurity in the UK. It explains the nature of the cyber threat, including how cyber attacks work. It describes the policy and regulatory frameworks, as well areas of reform such as 'ethical hacking'.

Research Briefing

London: House of Commons Library, 2024. 68p.

By Carter, Sarah R.; Wheeler, Nicole E.; Chwalek, Sabrina; Isaac, Christopher R.; Yassif, Jaime

From the document: "Rapid scientific and technological advances are fueling a 21st-century biotechnology revolution. Accelerating developments in the life sciences and in technologies such as artificial intelligence (AI), automation, and robotics are enhancing scientists' abilities to engineer living systems for a broad range of purposes. These groundbreaking advances are critical to building a more productive, sustainable, and healthy future for humans, animals, and the environment. Significant advances in AI in recent years offer tremendous benefits for modern bioscience and bioengineering by supporting the rapid development of vaccines and therapeutics, enabling the development of new materials, fostering economic development, and helping fight climate change. However, AI-bio capabilities--AI tools and technologies that enable the engineering of living systems--also could be accidentally or deliberately misused to cause significant harm, with the potential to cause a global biological catastrophe. [...] To address the pressing need to govern AI-bio capabilities, this report explores three key questions: [1] What are current and anticipated AI capabilities for engineering living systems? [2] What are the biosecurity implications of these developments? [3] What are the most promising options for governing this important technology that will effectively guard against misuse while enabling beneficial applications? To answer these questions, this report presents key findings informed by interviews with more than 30 individuals with expertise in AI, biosecurity, bioscience research, biotechnology, and governance of emerging technologies."

Nuclear Threat Initiative. 2023. 88p.

By SLEDJESKI, CHRISTOPHER L.

From the document: "Artificial Intelligence (AI) brings many benefits, but disruption of AI could, in the future, generate impacts on scales and in ways not previously imagined. These impacts, at a societal level and in the context of critical infrastructure, include disruptions to National Critical Functions. A prioritized risk-based approach is essential in any attempt to apply cybersecurity requirements to AI used in critical infrastructure functions. The topics of critical infrastructure and AI are simply too vast to meaningfully address otherwise. The National Institute of Standards and Technology (NIST) defines cyber secure AI systems as those that can 'maintain confidentiality, integrity and availability through protection mechanisms that prevent unauthorized access and use.' Cybersecurity incidents that impact AI in critical infrastructure could impact the availability, reliability, and safety of these vital services. [...] This paper was prompted by questions presented to MITRE about to what extent the original NIST Cybersecurity Risk Framework, and the efforts that accompanied its release, enabled a regulatory approach that could serve as a model for AI regulation in critical infrastructure. The NIST Cybersecurity Risk Framework was created a decade ago as a requirement of Executive Order (EO) 13636. When this framework was paired with the list of cyber-dependent entities identified under the EO, it provided a voluntary approach for how Sector Risk Management Agencies (SRMAs) prioritize and enhance the cybersecurity of their respective sectors."

MITRE CORPORATION. 2023. 18p.

By Harsha Moraliyage; Vidura Sumanasena; Daswin De Silva; Rashmika Nawaratne; Lina Sun; Damminda Alahakoon

The dark web has been confronted with a significant increase in the number and variety of onion services of illegitimate and criminal intent. Anonymity, encryption, and the technical complexity of the Tor network are key challenges in detecting, disabling, and regulating such services. Instead of tracking an operational location, cyber threat intelligence can become more proactive by utilizing recent advances in Artificial Intelligence (AI) to detect and classify onion services based on the content, as well as provide an interpretation of the classification outcome. In this paper, we propose a novel multimodal classification approach based on explainable deep learning that classifies onion services based on the image and text content of each site. A Convolutional Neural Network with Gradient-weighted Class Activation Mapping (Grad-CAM) and a pre-trained word embedding with Bahdanau additive attention are the core capabilities of this approach that classify and contextualize the representative features of an onion service. We demonstrate the superior classification accuracy of this approach as well as the role of explainability in decision-making that collectively enables proactive cyber threat intelligence in the dark web. 

IEEE Access, vol. 10, pp. 56044-56056, 2022,