Get Ready for Augmented Security Operations

You can't fix a broken SOC with agentic AI agents. But you can fix the SOC to get it ready for AI.

An agentic AI platform will not fix the data crisis plaguing the SOC

Find out if you're ready to deploy one, and whether to build that readiness yourself or buy it.

Before you go shopping for an agentic engine, you need to know whether your SOC is ready to run one. That is not a philosophical question. It is an engineering question, and it has a measurable answer. This research gives you the diagnostic to find it.

We assess your operations across four domains: the quality of your data, the determinism of your logic, the readiness of your workflow, and the maturity of your governance.

The output is a decision, not a roadmap. And it is a decision with a third option that the market has not yet resolved: do you build the foundation yourself, do you hand it to a managed provider who already has, or do you buy agentic software and assume that the vendor has solved the engineering problem for you?

This research will help you understand which of those three paths is honest given where you actually stand, and why the third option carries risks that the sales deck will not mention.

Fred Chagnon
Principal Research Director
Security & Privacy
Info-Tech Research Group

Executive summary

An overloaded SOC is not a people, process, or tool problem – it's a data engineering crisis.

Before you spend a dollar on AI tooling in response to this crisis, you need an honest picture of where your operations actually stand. Use that evidence to make a confident, defensible decision about whether to build your augmentation capability in-house or buy it from a managed provider. That decision, made with clear eyes on your actual operational maturity, is the foundation everything else is built on.

The traditional human-led SOC has been mathematically eliminated from relevance

The traditional human-led SOC has been mathematically eliminated from relevance

Everyone says AI is the solution, but security leaders don’t trust AI

• The Velocity of Failure: The fear isn't just that the AI will be wrong – it’s that it will be wrong at machine speed.
  
• The "Black Box" Liability: If a CISO cannot explain the reason behind an automated, yet “bad” decision to the board, the result is a career-ending event.
  
• The "God-Mode" Paradox: We are granting AI agents high-level API access to sensitive tools (EDR, IAM, firewalls) without a permission broker.
  
• Contextual Blindness: An AI often sees the threat but forgets the mission. Without vetted business context, the machine might treat an industrial controller and a multi-function printer with the same blunt force containment action.

The "human-in-the-loop" requirement isn't a strategy; it’s a symptom of engineering failure.

74% of organizations are actively limiting AI autonomy in their SOC until explainability improves.
Kiteworks, 2026

Only 5% of CISOs feel fully prepared to contain a compromised or "rogue" AI agent once it starts acting in their environment.
Cybersecurity Insiders, 2026

Only 14% of organizations allow AI to take independent remediation actions. The vast majority (70%) still require a human to click "approve" before the AI can act.
Kiteworks, 2026

If your logging isn’t pristine, you’re not ready for AI in the SOC

If you ask an agent to act on data that is stale, sterile, or structurally ambiguous, you’re asking for hallucinations.

• Stale Logs: Telemetry that arrives in 15-30 minute batches is sufficient for human forensics but useless for autonomous containment.
• Sterile Logs: Raw logs often lack business context. An agent cannot determine risk if it doesn't know if a targeted IP belongs to a guest Wi-Fi printer or a domain controller.
• Ambiguous Logs: Without a standardized schema (like OCSF), different tools use different labels for the same entities. This forces the AI to waste compute cycles on translation and guesswork, often leading to hallucinated correlations between unrelated events.
• Noisy Logs: High-volume, low-value heartbeat logs clutter the AI’s finite context window. This noise tax pushes critical security signals out of the agent's active memory, causing it to miss the actual threat.

Data Layer – The fuel of the SOC machine
Where raw telemetry is collected from endpoints, network, cloud, and identity sources.
SIEM/Telemetry Pipelines (EDR, XDR, ITDR telemetry)

If your decision logic isn’t deterministic, you’re not ready for AI in the SOC

If an agent can’t explain the reasoning behind its decision, it should not have the authority to act.

• The "Black Box" Trap: Traditional risk scores (1-100) are sterile. If an agent inherits a "90/100" score without the underlying reasoning path, it is forced to guess the context. This leads to a reasoning hallucination where the AI invents a threat to justify the score it was given.
• Contextual Blindness: Without a standardized schema (like OCSF), the AI wastes finite context window cycles translating different labels for the same entity. This "noise tax" pushes critical security signals out of active memory.
• The Token Spiral: Every "thought" has a price. Using a high-cost large-language model to triage "low-fidelity trash" data is a fiscal liability. If your analytics aren't pruned, you will face an uncontrollable cost overrun through wasted compute.

Logic Layer – The spark of the SOC engine
Where data is transformed into a decision verdict through reasoning, correlation, and risk scoring.
Threat Detection Engines

If your response actions aren’t codified, you’re not ready for AI in the SOC

If a playbook requires a human to "just know" when to skip a step, the AI will fail at machine speed.

• The "No-Kill" Zone Gap: Humans know that "locking out a doctor in the ER" is a non-starter. Without explicitly defined business guardrails, the agent will prioritize the security event over the business mission, leading to technically correct but operationally fatal actions.
• Monolithic Playbook Failure: 40-page PDF runbooks are for humans. To an AI, these are "ambiguous maps." You must deconstruct these into deterministic atomic operations – code-based steps where there is zero room for "probabilistic improvisation.”
• The Reversibility Requirement: Traditional workflows lack a "kill switch." If an action isn't technically reversible, the agent should never be given autonomous control. Safety interlocks must be engineered into the workflow, not audited after the outage.

Workflow Layer – The SOC Transmission
Where decisions are translated into actionable, deterministic response steps.
SOAR, Playbooks

If actions aren’t strictly governed, you’re not ready for AI in the SOC

If you grant an agent the power to act without an architectural “veto,” you have engineered a machine-speed liability.

• Unmanaged Blast Radius: Traditional permissions tend to be binary – an analyst is usually an admin or user. An AI agent should have more discrete entitlements that limit its power based on the specific risk of the command it is sending (principle of least privilege).
• Irreversibility: Isolating a laptop is a "Low-Stakes" action because it can be reversed; wiping a production database is "High-Stakes." Without a map of what can be "undone," you cannot safely grant the agent autonomous permission.
• Bypassing the "Kill Switch": In a manual SOC, the human is the ultimate safety interlock. If you haven't engineered a decision proxy that can "veto" an agent's request in real-time, you are allowing a probabilistic model to make high-consequence business calls without oversight.
• The "Reasoning" Audit Trail: If an action causes an outage, the CISO must be able to explain why it happened. Every action must be locked to an immutable chain of custody from the initial alert to the final API call.

Policy Layer – The Guardrails
Where entitlement actions are checked, governed, and audited across endpoints, networks, cloud, and identity.
API Connectors/Safety Interlocks/Permission Brokers

Case study: The failure of unchecked autonomy

Blindly offloading response authority to an AI without a deterministic data schema transforms a security tool into a high-speed engine for operational self-sabotage.

INDUSTRY: Financial

SOURCE: Dark Reading, 2026

Info-Tech’s methodology for assessing your security operations for AI augmentation

Case study: The success of augmented intelligence

AI achieves its highest ROI as a “cognitive force multiplier” that directs the human gaze, reducing discovery time by up to 36% without surrendering control of critical infrastructure.

INDUSTRY: Food Manufacturing

SOURCE: Dark Reading, 2026

You benefit from this work even before an agent is deployed

Don't spend a dollar on AI until you know if your SOC is ready for it

This assessment delivers four concrete outcomes before you commit to a single tool purchase:

• A documented build or buy recommendation supported by evidence across 16 assessment questions — so your decision has a defensible rationale, not just a vendor's pitch.
• A scored readiness baseline across data, logic, workflow, and policy that can be remeasured after gap closure to track real progress.
• A prioritized gap closure action list that tells you exactly what needs to be fixed, and whether it's faster to fix it yourself or buy your way out of it.
• Protection from the most expensive mistake in security technology: Deploying a high-performance AI agent against contaminated data and broken processes, then paying twice to fix the problems you should have caught here first.

Augmented Security Operations Assessment

This assessment will step you through the following:

• Data — Can the AI actually see and understand your environment?
• Logic — Is there a repeatable methodology for the AI to augment?
• Workflow — Is the plumbing ready for bidirectional AI communication?
• Policy — Is the organization ready to trust and audit an autonomous system?

At the end of each domain, you will receive a build or buy recommendation based on your answers.

Security leaders are highly motivated by an AI SOC

However, the studies show a massive disconnect between intent and execution.

• The Intent: 85% of CISOs view AI as a key enabler (Microsoft), with 60% planning an evaluation this year (Prophet Security, 2025).
• The Execution Gap: 42% of SOCs attempt to use AI "out of the box" with zero customization (SANS Institute, 2025).
• The Result: AI/ML tools currently rank at the bottom of the satisfaction list for SOC technologies (SANS Institute, 2025).

We are witnessing a rush to adopt “autonomous” tools by organizations that still rely on “manual” data and logic.
If we don't fix the underlying information and logic gaps identified in Phases 1 and 2, the next 12 months will be defined by expensive, high-speed hallucinations rather than defensible security outcomes.

The four layers of ASOC failure

High-speed autonomy cannot be built on a foundation of unmanaged data and intuitive logic.

“If you want effective analysis and containment [on an incident], you have to talk to all of these different siloed technologies and come up with a complete picture of what happened. But if you're missing any of these layers, you're painting a picture, but it’s incomplete."
– Jawed Ahmad, Author
Augmented Security Operations

1.1 Assess SOC readiness for AI

Download the Augmented Security Operations Assessment Tool

1 hour

Move through the 17 points in the ASOC readiness assessment. For each item, facilitate the following three-step loop:

• The Reality Check: Ask the specific owner (data, process, or architecture) if the capability is documented, automated, and repeatable.
• The "Why" Context: Read the "Why is this important?" column aloud. This ensures the CISO understands that a "NO" isn't just a missed feature – it’s a machine-speed risk.
• The Binary Verdict: Record the YES or NO. Do not allow "Partial" or "In-Progress" answers. If it isn't functional today, it’s a NO.

1.1 Assess SOC readiness for AI (cont’d)

Once a section is complete, or if the "NO" count becomes overwhelming, trigger the strategic realization protocol:

• Read the "BUY" Column: If the team lacks the "will or skill" to remediate a layer (e.g. data normalization), read the corresponding strategic realization text aloud.
• The Pivot Decision: Force a candid discussion: “Do we spend the next six months fixing the plumbing (BUILD), or do we buy a provider who has already sanitized the data (BUY)?”