By augmenting the skills of their human security analysts with machine learning capabilities, organizations can boost the efficiency of their SOCs and stay ahead of evolving cyberthreats.
It's January 2019 and cybercriminals are stealing your customer data. How will you use AI to execute your incident response plan and master the basics to avoid future incidents?
Researchers have shown how generative adversarial networks (GANs) can be applied to cybersecurity tasks such as cracking passwords and identifying hidden data in high-quality images.
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Generative adversarial networks are neural networks that compete in a game in which a generator attempts to fool a discriminator with examples that look similar to a training set.
Most organizations around the world lack a consistent incident response plan and thus are unprepared to manage the repercussions of a cyberattack, according to a recent Ponemon report.
By implementing orchestration and automation (O&A), security leaders can deliver the real-time threat intelligence their understaffed analyst teams need to punch above their weight.
To defend their confidential data from increasingly sophisticated cybercriminals, security teams must leverage machine learning to perform analytical tasks that are too tedious for humans to complete.
A recent Cisco study found that more firms are responding to cybersecurity news headlines by investing in artificial intelligence (AI) solutions to safeguard data.
Instead of dismissing experts who warn of impending cybersecurity disasters, business leaders should thoroughly investigate the issue at hand and prepare a response to minimize the potential damage.