Acronis International Gmbh

Candid Wuest

Stanislav Protasov

Serg Bell

Philipp Gysel

Dinil Mon Divakaran

Igor Milosevic

Irina Lukasheva

Kenneth Nwafor

Systems and methods implement artificial intelligence to automatically generate malware detection rules. In a first phase, an AI model is trained on large amounts of data, so the AI model can learn to distinguish between benign applications and different malware families. In a second phase, the AI model is queried with new malware samples, and systems and methods propose new malware detection rules for those samples.

AI assisted generation of rules for malware detection with transformer attention mechanism

Systems and methods for verifying a production system automatically by testing a mirror copy of the production system on a testing computer. The system includes a mirror update transporter to deliver a mirror update from the production system to the mirror system, a mounting module to apply the mirror update to the mirror system, a testing computer on which the mirror system is running, a testing module to automatically execute a set of tests on the mirror system, and a communication module to communicate the results of the tests.

System and method for evaluating active backups using penetration testing

Systems and methods for the protection of information systems utilize business impact analysis (BIA) data to assess the risk of security mitigation operations. A detected security incident is enriched using BIA data. A proposed mitigation action and a risk of implementing the proposed mitigation action are determined using the enriched data so that an administrator user can understand the impact or risk to the business for the proposed mitigation action.

Systems and methods of using business impact analysis data to assess the risk of security mitigation steps

Systems and methods for detecting a phishing attack in an email message. The method includes intercepting and evaluating an email, and generating a reputation score based on the evaluation of the email message. If the malicious component is detected, the email is blocked, if not, a trust score is generated. A determination is made whether the email is generated by AI, based on LLM. A certainty score is generated which is indicative of intentions and context of the email message being malicious. When the certainty score is higher than a threshold, a combined score by combining the reputation score, the trust score, and the certainty score. When the combined reputation score is higher than a threshold, the email is blocked, and if lower, a summary is generated by a summary AI engine. Based on a comparison with known malware summaries, the email is flagged or blocked.

Detection of malicious phishing emails

Systems and methods for verifying a production system automatically by testing a backup copy of the production system. The system comprises a backup transporter configured to copy or move a set of backup files from the backup generated of the production system and replicate the set of files on a testing computer, a mounting module configured to instantiate a copy of the production system on the testing computer, and a testing module configured to apply a set of automatic pre-defined tests to the application on the testing computer to analyze the application for vulnerabilities and defects, and generate a list of results of execution of each of the pre-defined tests on the testing computer, the list of results indicative of the vulnerabilities and defects.

System and method for automated dynamic penetration testing on active backup system

Systems and methods for simplified software backup. Generative artificial intelligence (AI) based on a large language model (LLM) is utilized to determine a backup restore operation for a backup request for a target system using a metadata tracked during a previous backup of the target system, and execute the backup restore operation to satisfy the backup request.

Systems and methods of using generative AI to simplify backup software interactions

The present disclosure relates to a system and method of automatically updating the set of security controls in the production environment using AI based on historical data generated in the test management system TMS during the system's testing in the testing environment including information about its elements, their properties, testing environment, its characteristics, and security controls with their settings. Once the AI has sufficient historical data from a testing environment, every time a change is detected to the system in the production environment, its elements, their properties, or at least one characteristic of the production environment, the AI system makes a recommendation to update the set of security controls in the production environment.

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Bassersdorf, Switzerland

Candid Wuest