A recent study conducted by the Enterprise Strategy Group (ESG) reveals that Generative Artificial Intelligence (Generative AI) is shaping the future of cybersecurity, with professionals expressing cautious optimism about its potential.
But what exactly is Generative AI? It is a branch of Artificial Intelligence that focuses on the ability to create original and autonomous content. This technology uses advanced algorithms to generate unprecedented creations, such as texts, images, music, virtual interactions, speeches and even programming codes. Unlike conventional artificial intelligence, which is programmed to perform specific tasks, generative AI is capable of creating something new and unexpected. It allows us to explore new possibilities and stimulate creativity, paving the way for innovations in many areas.
Generative AI: A double face
The study, carried out between November 2023 and November 2023 with professionals in North America, indicates that 87% recognize the potential of Generative AI to optimize security. However, these same professionals also recognize that the same tools can be used by criminals for more sophisticated attacks.
Governance and Policies for Responsible Adoption
To mitigate the risks, 75% of organizations are developing specific governance policies for Generative AI in the field of cybersecurity. Despite the optimism, the survey points to significant challenges. 70% of respondents highlighted the difficulty of integrating Generative AI into existing infrastructures, while 60% mentioned the risks of prejudice and the need for ethical considerations. Striking a balance between innovation and security is key to harnessing the full potential of this technology in cybersecurity and the military field.
“The Silent Revolution”: Generative AI is transforming the Military Field
Generative Artificial Intelligence (generative AI) is being applied in various areas of the military. Here are some of them:
1 – Lethal Autonomous Weapon Systems (LAWs): Lethal Autonomous Weapon Systems (LAWS) are a special class of emerging weapon systems. They incorporate machine learning and other types of Artificial Intelligence so that they can make their own decisions and act without direct human intervention. Here are some important points about these systems:
- Definition: LAWS are designed to seek out, identify and attack targets, including human beings, employing lethal force without any intervention from a human operator. Difference from Automated Systems: Unlike the highly automated defense systems used today (which are designed to shoot down incoming missiles or artillery shells), LAWS would operate without strictly rigid limitations in spatial and temporal terms.
- They could learn to adapt their operation to respond to changes in the circumstances of the environment in which they are employed.
- Not yet employed on the battlefield: although they have not yet been used in combat, LAWS research and development is attracting considerable interest and funds. They are likely to become a feature of warfare in the future.
- Ethical and legal issues: The use of LAWS raises major ethical and legal questions. Who would be responsible for their actions? How do we ensure that they respect International Humanitarian Law (IHL)? Call for Careful Assessment: The International Committee of the Red Cross (ICRC) is calling on states to assess the potential human cost and IHL implications of these new weapons technologies. In short, LAWS represent a crucial area of research and debate, with profound implications for global security and military ethics.
2 – Reconnaissance and Surveillance: AI is used to analyze satellite images, drones and cameras on the battlefield. It identifies targets, detects enemy troop movements and provides essential information to commanders.
3 – Tactics and Strategies: AI can create tactics and strategies that are superior to those of a human player. For example, the deep learning algorithm AlphaGo defeated one of the world’s best players in the Chinese strategy game Go, demonstrating its potential in war scenarios.
- AI is used to analyze satellite images, drones and cameras on the battlefield. It identifies targets, detects enemy troop movements and provides commanders with essential information. AI algorithms can process large volumes of data quickly, enabling accurate terrain assessment, identification of hostile targets and continuous monitoring.
- Data and Intelligence Analysis: AI is employed to analyze intelligence data, such as communications intercepts, field reports and information from open sources. It helps identify patterns, trends and potential threats, allowing strategists to make informed decisions.
- Simulations and Modeling: AI can also be used to create simulations of combat scenarios. This allows different strategies and tactics to be tested without real risks. AI models can predict the outcome of different actions and help optimize battle plans.
- Real-time decisions: AI systems can provide real-time recommendations to commanders during military operations. They evaluate contextual information, such as weather conditions, enemy movements and resource availability, to support quick and effective decisions.
4 – Logistics and Resource Management: Generative AI is also being applied to aid logistics and the administration of military resources, optimizing fleet maintenance and other processes.
- AI assists in the management of military resources, optimizing the use of supplies, transport, maintenance and personnel. It can predict future demands and ensure that resources are available when and where they are needed.
Conclusion:
Generative AI has the potential to revolutionize, but it requires careful planning and implementation. Security professionals, for example, express cautious optimism about the potential of generative AI to strengthen cybersecurity defences, recognizing its ability to improve operational efficiency and threat response.
However, Generative Artificial Intelligence still faces some significant challenges such as:
- Computational Infrastructure: Generative AI requires substantial computational resources to train and run complex models. Intensive processing can be an obstacle, especially for organizations with hardware limitations.
- Sampling Speed: Generating samples for generative models can be slow, especially for more advanced models. Optimizing generation speed is an area of active research.
- Data Quality: Generative models rely on large volumes of high-quality data to learn and create meaningful content. Bad or insufficient data can negatively affect results.
- Data licenses: Obtaining data for training generative models can involve licensing issues. Ensuring that data is used legally and ethically is key.
These applications show how generative AI is shaping the future of security and defense at a global level.