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How AI and ML can Improve Cybersecurity in 2022
Leading e-commerce companies are training their cybersecurity analysts on transaction fraud detection systems and collaborating with vendors to detect identity spoofing and the use of stolen privileged access credentials.
Fremont, CA: Cyberattacks are becoming more sophisticated, targeting multiple threat surfaces at the same time and employing a wide range of techniques to avoid detection and gain access to valuable data. Bad actors' preferred attack strategy is to use various social engineering, ransomware, phishing, and malware techniques to obtain privileged access credentials in order to circumvent Identity Access Management (IAM) and Privileged Access Management (PAM) systems.
Ways AI will improve cybersecurity in 2022
Detecting Transaction fraud – According to CISOs, the pandemic's effects on e-commerce sales are the primary motivator for investing in AI and ML-based transaction fraud detection. Transaction fraud detection is intended to provide real-time monitoring of payment transactions through the use of machine learning techniques to identify anomalies and potential fraud attempts. Furthermore, machine learning algorithms are being trained to detect login processes and prevent account takeovers (ATOs), which are one of the booming areas of online retail fraud today.
Leading e-commerce companies are training their cybersecurity analysts on transaction fraud detection systems and collaborating with vendors to detect identity spoofing and the use of stolen privileged access credentials. Identifying behaviors that are inconsistent with legitimate account holders also aids in the prevention of impersonation and stolen credential attacks. As CISOs and CIOs seek a single AI-based platform to scale and protect all transactions, fraud detection, and identity spoofing are converging. Equifax purchased Kount in 2021 to broaden its digital identity and fraud prevention solutions footprint. Accertify, Akamai, BAE Systems Cybersource, IBM, LexisNexis Risk Solutions, Arkose Labs, Microsoft, NICE Actimize, and others are among the leading vendors.
Identity Proofing – To defraud the institution and potentially breach its systems, bad actors attempt to create false identities and privileged access credentials with banks, financial services, educational institutions, and health care facilities. Identity proofing reduces fraud by verifying new customers' identities when they apply for care, or services, account openings, and balance transfers for new accounts. The adoption of AI and ML in the identity proofing market is diverse, including identity affirmation and identity proofing tools. In order to assess the authenticity of photo IDs and related photo-based documents, ML algorithms use convolutional neural networks, applying attack detection techniques to an image before attempting to match it to the photo ID.
Identity proofing and affirmation are both required to reduce fraud, which is one of the challenges that vendors competing in this market are addressing through API-based platform integration. Furthermore, as a result of the pandemic, identity-proofing vendors are experiencing exponential growth, with venture capital firms investing heavily in this area.