The New Arms
Race: Writing SLAs for Autonomous Combat
For years, SLAs
gave us clean metrics, Mean Time to Detect (MTTD), Mean Time to Respond (MTTR),
time-to-resolution. These numbers looked great in board meetings and satisfied
compliance checkboxes.
But in 2025,
they’re mostly fiction.
Take MTTR. We
used to measure how long a human analyst needed to investigate and contain a
breach. But AI doesn’t operate on human time. A generative AI system can scan
for vulnerabilities, exploit one, pivot across endpoints, and exfiltrate
terabytes of data, in under a minute.
By the time
you're even aware of the breach, your SLA clock is already irrelevant.
Detection Is
Broken, Too
Static,
rule-based detection systems weren’t built for polymorphic malware or
deepfake-powered phishing campaigns. Today’s AI-generated threats evolve in
real time, mimic legitimate behavior, and bypass traditional filters without
resistance.
Alert fatigue?
It’s become full-scale operational failure. Human analysts can't triage
thousands of daily alerts, especially when many are triggered by AI-crafted
decoys designed to overwhelm and distract.
The threat
landscape has changed. Our contracts haven’t.
What Modern
SLAs Must Actually Measure
It’s time to
throw out outdated metrics and rewrite the rulebook. Here’s how:
1. Engineer
SLAs for an Autonomous Defense Era
Measure what
matters in an AI-driven battlefield. Think:
- Mean Time to AI Model Adaptation
- AI-Assisted Remediation Success
Rate
- Autonomous Containment Response
Time
These show
whether your defenses are evolving faster than the attackers, not how fast a
human reacts after the damage is done.
2.
Predictive, Not Reactive
The best
defense is knowing what’s coming. SLAs should now include:
- Behavioral anomaly prediction rates
- Pre-attack threat signature
detection
- False positive suppression accuracy
If your SLA
doesn’t measure foresight, it’s already falling behind.
3. Response
and Recovery Must Be Autonomous
Human-led
response? That’s a postmortem, not a defense. SLAs must prioritize:
- Automated containment scope and
speed
- Self-healing system success rates
- AI-led data restoration validation
In healthcare
especially, response speed isn’t just about uptime, it’s about patient safety.
4. Outcomes
Over Alerts
Don’t count how
many alerts you saw or how quickly they were closed. Focus on what actually
matters:
- Reduction in successful AI-driven
phishing
- Uptime continuity during live
threat events
- Sustained integrity of sensitive
data under attack
This isn’t
box-checking, it’s real-world damage prevention.
5.
Transparency Is Non-Negotiable
Black-box AI
tools introduce unacceptable risk. Modern SLAs must demand:
- Explainability metrics (decision
traceability)
- Auditability guarantees
- Regulatory transparency thresholds
(HIPAA, PCI, etc.)
If your AI
can’t explain its decisions—or its silence—it has no business in critical
infrastructure.
6. SLAs Must
Be Living Documents
The AI threat
landscape mutates weekly. Your SLAs must evolve just as fast. That means:
- Quarterly reassessments
- Threat intelligence synchronization
- Continuous feedback from real-world
telemetry
If your SLA
hasn’t changed in six months, it’s not just outdated, it’s a liability.
Time’s Up
for Old Thinking
This isn’t
about adding an “AI clause” to an old framework. It’s about admitting we’re
playing a new game, with new physics, new adversaries, and far higher stakes.
Legacy SLAs
offer the illusion of control in a world where control must be
redefined, programmatically, probabilistically, and in real time.
Cybersecurity
leaders must stop measuring how fast humans react and start measuring how
effectively machines defend. Because in an AI-powered battlefield, slow
equals breached.
Brian Wilson is a cybersecurity strategist and
founder of GT1, a consultancy focused on adapting digital defense frameworks
for the AI era. He advises healthcare and enterprise organizations on
reengineering their security infrastructure for speed, autonomy, and resilience.
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