Application Security measures - Protecting user data in AI times

AI cyber security measures specifically add a layer of protection at application level, to detect fraudulent application usage, fake accounts, risky transaction profiles and much more.

The goal is to provide extra protection for user personal data, unauthorized accesses and transactions, severe financial loss (corporate; individual), reputational damage and/or data theft and loss.

Given our continued rise in digital use for personal, financial and professional life, security measures have evolved quickly in the last few years, in response to growing cybercrime.

Security measures online include older authentication tools (keywords), new ones such as 2FA and MFA (2-factor authentication / multiple factor authentication), as well as modern ones such as AI type cybersecurity, adaptive MFA and behavioral biometrics.

AI application and account protection works differently. It does not “break” the UX at any user touch point, rather, it monitors the usage patterns of the current session (inclination of the device, IP address, IP change, keystroke speed, etc.)

By comparing with unique historical patterns of previous sessions, the AI can monitor risk level, with associated mitigations. Cue in: Adaptive MFA.

An evolution from MFA, adaptive MFA is enabled by AI: Instead of a “one-size-fits-all” 2FA or MFA tool, the app takes the estimated risk level of each unique session, its specific transactions and usage, to preset configuration (alerts, freeze, extra 2FA requests etc).

AI cybersecurity represents an extra, hyper-flexible tool, capable of flexible analysis, with no/low intrusion on user UX at least for perceived low level risk.

A random data point around an account usage (like say, a new IP address) may not mean much, the user may have gone on a business trip, and by itself this would never cause any alarm.

However, if that same user changed IP addresses often recently, logs on at odd hours, and types much faster, in that case the AI can synthesize the risk profile and act early on it, in a nuanced way.

By learning typical usage patterns of the normal user, the AI can very quickly detect anomalous behavior, and raise protection protocols accordingly (from asking a “Smart MFA” confirmation for a bespoke transaction, to account freezing till manual review). Behavioral biometrics security measures Behavioral biometrics are at the heart of the AI security measures, offering a much finer and less intrusive monitoring than only “coarse” steps such as 2FA or MFA which can also be limited in their effectiveness.

Added to the general MFA toolkit, behavioral biometrics allow for a much finer account security. They can immediately bring granularity to existing MFA tools, allowing for much finer configuration.

As a result of ability to work immediately on top of existing MFA, AI-led behavioral biometrics also represent a unique opportunity to upgrade that general standard to “Smart MFA”. A low-risk profile may receive an ad-hoc MFA request for a given transaction that would not normally require it.

A high-risk profile may see the account instantly frozen until manual validation, raising enormously the overall security measures around the application, UX, and risk.

AI type behavioral biometrics also provide much more preventive security measures, a substantial benefit in a landscape of growing cybersecurity litigation. By acting early, as soon a profile usage clashes with the normal user, they can prevent fraud and financial loss before it happens.

Monitoring mouse patterns or frequency of failed logins can be surprising, but useful data in building a synthetic person profile, with related risk level. This technology relying on unique patterns makes it much harder for a bad agent to reproduce.

Implementing advanced cyber fraud AI type security measures must meet data protection regulations such as GDPR in Europe, which restrict the capture and use of personal data.

It is vital for organizations to ensure their cybersecurity posture respects the laws and avoids data risk and financial risk.

Legislation around cybersecurity has often been lagging behind, considering the incredibly fast rate of evolution in cyber threats.

Of all times, legislation around cybersecurity has had to centrally weigh up trade-offs between data privacy and cybersecurity to the maximum benefit of the user.

Considering how scalable and effective AI-led cybersecurity can be and how anonymised the usage data monitored is, we can probably expect limited and reducing brake to adoption.

Meeting and exceeding existing data privacy and protection standards will however remain an evolving discussion and evolution.

As technology evolves the methods to secure accounts will continue to develop.

The integration of AI and behavioral biometrics will become more common in cybersecurity offering a protection both more thorough as well as less intrusive.

Highly privileged account management may also include more commonly in the future new forms of MFA, physical tokens or advanced/in-person biometrics.