The AI-MDR Gap: Are We Outsourcing Defense to Obsolescence?

Just caught the latest on The Hacker News about rethinking MDR in the age of AI, and it really hits home. For years, MDR was our safety net for the 'alert fatigue' problem—outsourcing the triage we couldn't handle. But if attackers are leveraging LLMs to automate polymorphic malware and generate unique payloads for every target, can legacy MDR actually keep up?

We're seeing AI-generated variants of exploits for recent criticals like CVE-2026-44963 (Veeam) popping up in honeypots within hours of disclosure. These variants have slightly modified hashes and obfuscation layers that slide right past standard EDR signatures and overwhelm the tier-1 analysts at most MDR providers.

I've started focusing on heuristic analysis internally to catch what the MDR misses. For instance, checking for high entropy in script blocks is a solid baseline for detecting AI-packaged code.

import math

def calculate_shannon_entropy(data):
    if not data: return 0
    entropy = 0
    for x in range(256):
        p_x = float(data.count(chr(x))) / len(data)
        if p_x > 0:
            entropy += - p_x * math.log(p_x, 2)
    return entropy

# Flag potential AI-obfuscated scripts if entropy > 7.5
susicious_script = "..."
if calculate_shannon_entropy(susicious_script) > 7.5:
    print("High entropy detected - potential AI obfuscation")


On the SIEM side, we're hunting for behavioral anomalies rather than just IOCs:

DeviceProcessEvents
| where FileName in~("powershell.exe", "python.exe")
| where ProcessCommandLine has "encodedcommand" or ProcessCommandLine has "base64"
| summarize count(), dcount(ProcessCommandLine) by DeviceId, bin(Timestamp, 5m)
| where count_ > 10 and dcount_ProcessCommandLine_ > 5 // Rapid variation in commands


Is anyone else seeing their MDR provider struggle with these high-volume, low-prevalence AI attacks? Or are the big vendors finally integrating automated response capabilities that match the attacker's speed?