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AI & Cybersecurity

AI can be used to both enhance and threaten cybersecurity. Here are some examples of how AI can be used in cybersecurity:

  1. Threat Detection: AI can analyze large volumes of data from network traffic, system logs, and other sources to detect and respond to cyber threats in real-time.
  2. Vulnerability Assessment: AI can be used to identify potential vulnerabilities in systems and networks, allowing for proactive measures to be taken to prevent cyber attacks.
  3. Malware Detection: AI can analyze patterns in code to detect and prevent malware, viruses, and other types of malicious software.
  4. User Behavior Analytics: AI can analyze patterns in user behavior to detect and prevent insider threats, such as employees who may be accessing sensitive data without authorization.
  5. Fraud Detection: AI can analyze patterns in financial transactions to detect and prevent fraud, such as credit card fraud or identity theft.

On the other hand, AI can also be used by cybercriminals to carry out attacks. For example:

  1. AI-Powered Malware: Cybercriminals can use AI to develop malware that can evade traditional cybersecurity measures.
  2. Automated Attacks: AI can be used to automate attacks, such as phishing or brute-force attacks, making them more effective and efficient.
  3. Deepfakes: AI-generated deepfakes can be used to impersonate individuals, such as executives or political figures, to carry out social engineering attacks.
  4. Data Poisoning: Cybercriminals can use AI to manipulate data and train models to make incorrect decisions or take malicious actions.

Therefore, it is important to ensure that AI is used responsibly and ethically in cybersecurity, and that measures are taken to prevent cybercriminals from using AI to carry out attacks. This requires ongoing research and development of AI-based cybersecurity tools and strategies, as well as collaboration between industry, government, and academia to stay ahead of evolving threats.

Artificial Intelligence is used in cybersecurity to prevent any threats based on data that unknowingly we make available to attackers. Through intelligence analysis on the web also for defensive purposes. Since “the best defense is attack”, it is advisable to gear up in advance by trying to understand the points of vulnerability and where the threats could come from. It is called “Open Source Intelligence“. It arises from a reasoning: the growing amount of information and data publicly available and related to a potential target (such as an individual or an organization) represents a facilitator element for the conduct of effective attacks.

Reversing the perspective, the opposite is also true: developing a clear vision of the type of data and information publicly exposed and relating to your organization (assets, top management, internal personnel, external personnel, teams responsible for the organization’s security, financial information). It represents an enabling factor for the development of preventive and proactive capabilities to combat the threat.


The theme is closely linked to the development of artificial intelligence. That is, algorithmic analysis to prevent any threats based on the amount of data that each of us also unknowingly makes available to attackers while browsing the Internet.

Every employee is an Internet user. He has passions, interests, semantic researches that are more or less unspeakable. Machine Learning allows you to analyze the behavior of each user and network traffic and independently learn what is normal for a user or for network traffic.

Companies can constantly monitor the authentication and access activities of each user, downloads, uploads, data transfers. The models learn on their own based on the activity of each user, they start immediately after installation and thus continue learning better and better and in total autonomy. The more time passes, the more precise the algorithm becomes. Abnormal behavior, based on what each user normally does, is highlighted as abnormal activity. With some cumbersome privacy questions.

Computer Vision and Cybersecurity

Computer vision can play an important role in enhancing cybersecurity by detecting and preventing threats that may not be visible to the human eye. Here are some examples of how computer vision can be used in cybersecurity:

  1. Video Surveillance: Computer vision can analyze video footage from security cameras to detect suspicious behavior, such as people entering restricted areas or carrying out unauthorized activities.

  2. Object Detection: Computer vision can be used to detect and identify objects in images and video, such as weapons or suspicious packages, that may pose a security threat.

  3. Facial Recognition: Computer vision can be used to identify individuals, such as employees or visitors, and determine whether they have permission to access certain areas.

  4. Anomaly Detection: Computer vision can analyze patterns in network traffic or system logs to detect anomalies that may indicate a security breach or cyberattack.

  5. Malware Detection: Computer vision can analyze images of code to detect and prevent malware and other types of malicious software.

  6. Document Verification: Computer vision can analyze images of documents, such as passports or IDs, to verify their authenticity and prevent fraud.

How to secure data with predictive algorithms

Painted as the weak link of business organizations: the end-points. These are the remote access points for employees – company PCs, tablets and smartphones – connected to home wi-fi networks. The boom in mobile work, thanks to the structural adoption of smart working, is reconverting the ability of “attackers”, often criminal organizations that derive dizzying income from cyber intrusions.

Thus a more sophisticated coverage capacity is required by companies that reaches the end-points, the terminals for accessing company software via VPNs. What experts call a “virtual machine” is gaining momentum. These are the multi-platform investments of all the big device manufacturers, from Hewlett-Packard to Intel to Dell, from Microsoft to Apple.

Each file is isolated. Kept in a “digital box” to be screened before being stored on a hard disk. Become a virtual machine. Each document is opened and isolated from the PC, protected from possible malware, and then connected to the company Intranet.

virtual machine