The hardest problems are not really about AI. Zero trust, least privilege, alignment, continuous validation, optimization, and cost efficiency have to come first. AI comes after all of them. This blog outlines principles of an AI-driven MDR that can operate safely, efficiently, and at scale.
Current security operations incidents take too long to investigate and tools don’t talk to each other the way they should. There needs to be a better way to get an operational layer that’s supposed to tie it together.
An agent can generate hypotheses, query systems, weigh evidence, and act at a speed no human team can match. The question is no longer simply how to produce more analysis. It becomes how to turn a flood of machine-generated analysis into decisions that can be trusted, governed, and defended.
When Zero Trust gained momentum, similar predictions followed. Now, with AI changing how quickly and cheaply software can be built, SIEM is once again one of the first security categories people expect to be commoditized. This time, however, the argument may carry more weight because AI is changing the underlying economics of security software rather than simply introducing another buzz word.
The hardest problems are not really about AI. Zero trust, least privilege, alignment, continuous validation, optimization, and cost efficiency have to come first. AI comes after all of them. This blog outlines principles of an AI-driven MDR that can operate safely, efficiently, and at scale.
Token economics became a serious conversation the moment the industry realized we'd been getting an unreasonably generous discount from AI vendors. Now that free tiers are shrinking and usage caps are showing up everywhere, teams have started rationing tokens the way they once rationed analyst hours.
Every SOC runs on prioritization and there are always more alerts than analysts can investigate thoroughly. That's an unavoidable reality. But the incidents a SOC waves through as low-risk aren't necessarily noise. They may be its most valuable untapped threat intelligence.
Choosing the right MDR is critical because it becomes your 24/7 monitoring capability, investigation team, escalation layer, and force multiplier for the stack you already own.
This is the argument we believe the security industry needs to have. Not just about whether AI works in the SOC (it does) but about who owns the SOC when AI runs it.
Current security operations incidents take too long to investigate and tools don’t talk to each other the way they should. There needs to be a better way to get an operational layer that’s supposed to tie it together.
An agent can generate hypotheses, query systems, weigh evidence, and act at a speed no human team can match. The question is no longer simply how to produce more analysis. It becomes how to turn a flood of machine-generated analysis into decisions that can be trusted, governed, and defended.
When Zero Trust gained momentum, similar predictions followed. Now, with AI changing how quickly and cheaply software can be built, SIEM is once again one of the first security categories people expect to be commoditized. This time, however, the argument may carry more weight because AI is changing the underlying economics of security software rather than simply introducing another buzz word.
The hardest problems are not really about AI. Zero trust, least privilege, alignment, continuous validation, optimization, and cost efficiency have to come first. AI comes after all of them. This blog outlines principles of an AI-driven MDR that can operate safely, efficiently, and at scale.
Token economics became a serious conversation the moment the industry realized we'd been getting an unreasonably generous discount from AI vendors. Now that free tiers are shrinking and usage caps are showing up everywhere, teams have started rationing tokens the way they once rationed analyst hours.
Every SOC runs on prioritization and there are always more alerts than analysts can investigate thoroughly. That's an unavoidable reality. But the incidents a SOC waves through as low-risk aren't necessarily noise. They may be its most valuable untapped threat intelligence.
Choosing the right MDR is critical because it becomes your 24/7 monitoring capability, investigation team, escalation layer, and force multiplier for the stack you already own.
This is the argument we believe the security industry needs to have. Not just about whether AI works in the SOC (it does) but about who owns the SOC when AI runs it.
Current security operations incidents take too long to investigate and tools don’t talk to each other the way they should. There needs to be a better way to get an operational layer that’s supposed to tie it together.
An agent can generate hypotheses, query systems, weigh evidence, and act at a speed no human team can match. The question is no longer simply how to produce more analysis. It becomes how to turn a flood of machine-generated analysis into decisions that can be trusted, governed, and defended.
When Zero Trust gained momentum, similar predictions followed. Now, with AI changing how quickly and cheaply software can be built, SIEM is once again one of the first security categories people expect to be commoditized. This time, however, the argument may carry more weight because AI is changing the underlying economics of security software rather than simply introducing another buzz word.
The hardest problems are not really about AI. Zero trust, least privilege, alignment, continuous validation, optimization, and cost efficiency have to come first. AI comes after all of them. This blog outlines principles of an AI-driven MDR that can operate safely, efficiently, and at scale.
Token economics became a serious conversation the moment the industry realized we'd been getting an unreasonably generous discount from AI vendors. Now that free tiers are shrinking and usage caps are showing up everywhere, teams have started rationing tokens the way they once rationed analyst hours.
Every SOC runs on prioritization and there are always more alerts than analysts can investigate thoroughly. That's an unavoidable reality. But the incidents a SOC waves through as low-risk aren't necessarily noise. They may be its most valuable untapped threat intelligence.
Choosing the right MDR is critical because it becomes your 24/7 monitoring capability, investigation team, escalation layer, and force multiplier for the stack you already own.
This is the argument we believe the security industry needs to have. Not just about whether AI works in the SOC (it does) but about who owns the SOC when AI runs it.
Current security operations incidents take too long to investigate and tools don’t talk to each other the way they should. There needs to be a better way to get an operational layer that’s supposed to tie it together.
An agent can generate hypotheses, query systems, weigh evidence, and act at a speed no human team can match. The question is no longer simply how to produce more analysis. It becomes how to turn a flood of machine-generated analysis into decisions that can be trusted, governed, and defended.
When Zero Trust gained momentum, similar predictions followed. Now, with AI changing how quickly and cheaply software can be built, SIEM is once again one of the first security categories people expect to be commoditized. This time, however, the argument may carry more weight because AI is changing the underlying economics of security software rather than simply introducing another buzz word.
The hardest problems are not really about AI. Zero trust, least privilege, alignment, continuous validation, optimization, and cost efficiency have to come first. AI comes after all of them. This blog outlines principles of an AI-driven MDR that can operate safely, efficiently, and at scale.
Token economics became a serious conversation the moment the industry realized we'd been getting an unreasonably generous discount from AI vendors. Now that free tiers are shrinking and usage caps are showing up everywhere, teams have started rationing tokens the way they once rationed analyst hours.
Every SOC runs on prioritization and there are always more alerts than analysts can investigate thoroughly. That's an unavoidable reality. But the incidents a SOC waves through as low-risk aren't necessarily noise. They may be its most valuable untapped threat intelligence.
Choosing the right MDR is critical because it becomes your 24/7 monitoring capability, investigation team, escalation layer, and force multiplier for the stack you already own.
This is the argument we believe the security industry needs to have. Not just about whether AI works in the SOC (it does) but about who owns the SOC when AI runs it.
