Most breaches in 2026 don't start with an attacker breaking through a firewall. They start with an attacker logging in — using a stolen password, a bypassed MFA prompt, or a session token lifted from an already-compromised device. The perimeter didn't fail. It was never involved. Identity is the actual attack surface now, and AI has made every step of compromising it faster, cheaper, and more convincing.
Multi-factor authentication is necessary. It's no longer sufficient.
MFA was built around the assumption that a second factor — a code, a push notification, a phone call — is hard for an attacker to fake. AI is quietly removing that assumption. In one widely reported case, a journalist cloned her own voice using an inexpensive, publicly available AI tool and used it to pass her own bank's phone-based identity verification. If a journalist testing the idea can clear that bar with off-the-shelf tools, a motivated attacker targeting a specific employee can too.
This doesn't mean MFA is worthless — it means credential-and-one-time-code verification alone is no longer the ceiling of a good identity security program. It has to be the floor.
Video calls and voice used to be proof. Now they're an attack surface.
For most of digital history, "I saw them on the call" or "I recognized their voice" was about as strong a verification as anything short of meeting in person. That assumption is breaking in real time, and not hypothetically — deepfake video and voice have already been used to authorize real, multi-million-dollar wire transfers by convincing an employee they were on a call with actual colleagues. The practical takeaway for identity security specifically: any process that treats "I recognized them" as sufficient identity proof needs a second, non-human-judgment-based check behind it.
What identity threat detection actually needs to catch
Not just failed logins. The attacks that matter now look legitimate at every individual step:
- An account takeover that uses a real, previously-stolen password — no obvious anomaly at the password layer.
- A malicious OAuth application a user grants access to during a routine-looking consent prompt, which then sits quietly inside Microsoft 365 or Google Workspace with legitimate-looking permissions.
- An unauthorized login from a new location that doesn't trip a simple geofence, because the attacker is routing through infrastructure close to the victim's usual location.
None of these involve malware. None of them look wrong in isolation. Catching them requires continuous monitoring across the full identity lifecycle — logins, OAuth grants, permission changes, unusual access patterns — not a one-time authentication check at sign-in.
This is exactly the gap Endpoint & Identity Protection is built to close
CyberFence's Endpoint & Identity Protection, delivered in partnership with Huntress, covers both sides of this problem together rather than as separate tools: 24/7 monitored endpoint detection and response across every device, and identity threat detection and response purpose-built for Microsoft 365 and Google Workspace environments — covering account takeovers, business email compromise, unauthorized logins, and malicious OAuth applications, backed by an AI-assisted security operations center working alongside human analysts around the clock. Continuous identity posture hardening runs in the background too, closing the misconfigurations that make an initial compromise easier in the first place.
Why this matters more for smaller businesses, not less
Attackers increasingly target under-resourced companies precisely because they assume identity monitoring at this level is an enterprise-only budget line. It isn't, when it's delivered through a managed partner rather than an in-house team you'd have to hire, train, and staff around the clock yourself.
Sources: SecurityWeek, "Five Cybersecurity Predictions for 2026: Identity, AI, and the Collapse of Perimeter Thinking"; Huntress platform documentation.