Account Trust
Built Into Every Login

Sift helps detect account takeover by evaluating each login and user session against real-time risk signals such as device intelligence, behavioral patterns, unusual account changes, and broader network insights that can reveal threats a single business may not see on its own. Based on that risk, teams can block a session, trigger step-up authentication, route it for review, or continue monitoring after login, helping stop account takeover before it turns into loss or customer churn.

MFA is an important control, but on its own it does not determine which sessions are actually risky or adapt well to evolving attack tactics. Sift adds a risk-decision layer on top of authentication by combining device, behavioral, velocity, and network signals to help teams decide when to allow access, when to trigger MFA, and when to take stronger action, so protection is based on real session risk rather than a one-size-fits-all challenge.

Sift is designed to apply friction selectively at the session level, so trusted users can move through login and post-login activity with less interruption while suspicious activity receives additional scrutiny. By combining real-time risk scoring with adaptive authentication and ongoing monitoring after login, Sift helps businesses challenge the moments that look risky without forcing every user through the same experience.

Sift is built to augment existing identity and authentication investments rather than replace them, with low-code integrations for leading CIAM platforms and flexible event-based implementation options for teams with custom stacks. Businesses can send key login and account events into Sift, use Sift risk signals inside their existing orchestration or authentication flows, and trigger actions such as allow, challenge, or review based on the level of risk.