Cybersecurity software tools are critical to protecting your businesses’ applications and data using encryption techniques, helping you discover risks in the early stage by implementing risk assessment and developing risk management strategies. Cybersecurity software is a cybersecurity product; some of the common products are antivirus, antispyware, firewalls, or virtual private networks software.
And as we’re heading into 2021, cybersecurity software tools are evolving. Cybersecurity software is integrated with artificial intelligence innovations. These cutting-edge technologies will help companies to be well-prepared for the innovative attacks of cybercriminals.
For all businesses looking for a new way to upgrade their cybersecurity approach, here are the 3 ways that ensure the effective use of AI in cybersecurity.
All businesses should use AI responsibly for cybersecurity.
AI enables businesses to respond faster to data breaches. As more and more criminals are using AI-based malware to attack, cybersecurity leaders must adopt AI-based cybersecurity software as a new tactic to respond to these attacks. All businesses should embrace this effort to ensure better cybersecurity practices for their own businesses.
Make sure that AI concepts are relevant to threat modeling and risk management
A recent Internation Data Corporation survey revealed that more than 2000 IT and LoB key-decision makers agree that many business organizations are adopting AI technology globally. Over a quarter of all AI initiatives are already in production, and more than one-third are in the advanced development stage.
However, it is essential and prudent to consider how cybercriminals might try to disrupt this growth and the stability of the newly integrated AI system. Cybercriminals are making cyberattacks like data poisoning attacks or backdooring of training models that are likely to be deployed against machine learning systems.
Threat models that incorporate AI systems are rare and often not yet mature. Business leaders should strive to develop methods to ensure artificial intelligence technology solutions are within the scope of risk management activities to mitigate such threats.
Facilitate the integrated privacy-preserving machine learning
Although machine learning and AI solutions development are becoming more popular, privacy implications have not yet to be widely explored. In particular, neural networks can remember specific examples from training data. As a result, the AI system may be subject to model inversion attacks, leading to data theft on which the model was trained.
If the model was trained on sensitive data, the attacker could recover information about the training data. The increasing risk of security breaches has led to an increasing need for people to adopt and develop privacy-preserving machine learning systems. These solutions will protect not only businesses but also consumers whose data is used to train models.
Integrating AI into cybersecurity isn’t an easy task. Business leaders have to identify the challenges of merging and developing solutions that enhance cybersecurity programs while maintaining ethics and protecting data privacy.