How Digital DNA Recognition Works: Fingerprinting Puts Invisible Protection to the Test
Device fingerprinting transforms enterprise SaaS security by streamlining login speeds and reducing account takeovers whilst protecting user access
The daily grind of workplace security has become exhausting. Enter your password, wait for the text message, type in the six-digit code, wait another 10 seconds for the system to verify, then finally access your work tools. This routine frustrates millions of employees whilst costs continue mounting – account takeover attacks rose 24% in 2024 and breaches now cost enterprises an average of $4.88 million per incident.
The Stakes for Enterprise Software
For B2B SaaS companies serving enterprise clients, this creates a double problem. Their customers face constant security threats whilst demanding faster, smoother access to work applications. SSOJet’s new device fingerprinting technology claims to solve both issues simultaneously – blocking threats in the background whilst speeding up legitimate logins.
The numbers suggest this approach works. Early adopters report preventing 14,000 unauthorised access attempts in the first month, reducing account takeover incidents by 98.3% and improving legitimate user login speeds by 40%. SSOJet estimates this saves customers an average of $4.2 million in potential breach costs.
These results matter because cybercriminals increasingly target B2B SaaS platforms, exploiting compromised credentials and system misconfigurations. Traditional security methods often fail to catch sophisticated attacks or create friction that slows down legitimate users.
How Digital DNA Recognition Works
Device fingerprinting moves beyond passwords and security codes. Instead of asking users to prove their identity, the system recognises the unique ‘DNA’ of their devices and behaviour patterns.
SSOJet’s system analyses over 300 device attributes in real-time – everything from browser settings and screen resolution to typing patterns and mouse movements. Machine learning algorithms build behavioural profiles that adapt to how real users work whilst detecting anomalies that signal impersonation attempts.
The technology runs invisibly. Users log in normally whilst the system compares their device characteristics and behaviour against stored profiles. Legitimate users proceed without extra steps. Suspicious activity triggers additional verification or blocks access entirely.
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