AdaptiveMFA

Overview

Development stage

DeployedSource: c) Deployed – The use case is being actively authorized or utilized to support the functions or mission of an agency.

High-impact designation

Not high-impactSource: c) Not high-impact

Impact justification

Not reported

Start date

2025-03-30 (day precision)Source: 2025-03-30T00:00:00

Withheld from public reporting

Not reported

Mission

Topic area

Administrative Functions

Operational functions

Cyber threat detection, Biometrics and identity Derived by keyword rules; see methodology

Problem the AI is intended to solve

Enhances security and the user experience, including: Behavioral Anomaly Detection - AI monitors user activity over time to establish a baseline pattern of their behavior. If a login attempt deviates significantly, such as logging in from a foreign country, unknown workstation, or disallowed IP address space, Okta can trigger Adaptive Multi-factor Authentication (aMFA) to block access. Adaptive MFA - Okta's AI-powered Adaptive MFA tailors security challenges based on risk level. Trusted users might only need a password, while high-risk users are prompted for biometric or application-based verification. Real-Time Threat Detection - Okta's integration with AI-driven threat intelligence platforms like CrowdStrike and Microsoft Defender enhance real-time visibility into threats, correlating data from endpoint, network, and identity layers. Access Governance with Intelligence - AI enables smarter access reviews and role recommendations. It detects unusual access rights, flags overprovisioned users, and automatically suggests changes. AI is integrated into DHS's identity and access management solutions to strengthen security and enhance the user experience.

Expected benefits

Adaptive Multi-factor Authentication (aMFA) introduces additional intelligence into Identity flows by taking into account the authentication context data during the authentication. Using the data, DHS is able to adapt security and authentication policies to enhance the security to DHS systems.

System outputs

The input includes: device, network, location, travel, IP, and external data from third parties and endpoint security integrations. The output are Risk ratings HIGH, MEDIUM, LOW for each authentication attempt which can be configigured to require stricted access comtrol policies.

Technology

AI classification

Classical / predictive MLSource: Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relatio…

System name(s)

The DHS Next Generation Authentication as a Service (DHSAuthPortal) System

Custom-developed code

No

Public source code

Not reported

Sourcing

How it was built

Contract and in-houseSource: c) Developed with both contracting and in-house resources

Vendor (as reported)

Okta

Vendors (standardized)

None on the standard list

A vendor is the supplier named by the agency. It does not identify the underlying model or AI technology.

Data and privacy

Involves PII

No

Privacy Impact Assessment

Not reported

Authorization to Operate

Yes

Demographic features

Not reported

Training and evaluation data

The system collects signal data during authentication to dynamically build behaviors of the user.

Federal Data Catalog

Not reported

Governance

0 of 8 minimum-practice questions answered. A blank answer means the agency reported nothing; it does not mean the practice is absent.

Potential impacts and how they were identified

Not reported