ai · security · skills

ai security skills:
human craft for machine speed.

Pressure-test the function. Build the defender’s skills. Guided by Ace.

Autonomy rises. So does the bar.On track
12345GovernanceOrg managementIAMSecurity monitoringInfra security & resilienceModel securityApp securityData securityRisk & provider assessmentAI dev & supply chainPrivacy & complianceIncident responseFoundationalStructuralProcedural
Human bottleneck82%
Safety net needed22%

Manual: The human is the safety net. Low autonomy asks little of your controls.

Why this app

Security and risk lead every survey of AI concern.

Every fear has an owner in the model. Pressure-test the function it belongs to, and the read starts there.

Why now

Six open problems. No fitted owner yet.

Lines from real enterprise incident rooms and AI-security surveys. Not product features. Gaps your people already feel that no classic control alone closes.

The gap · Incident room

Agent and human shared one account. The record shows a single actor and accounts for nothing.

Source: Proofpoint, State of AI Security 2025 (survey of 275 enterprise security and business leaders). Quotes are what respondents report, not measured outcomes on this site.

Prefer the full urgency arc? Why now on /threats →

Ace's watch

What changed.And what to learn because of it.

Every item names the one thing worth learning next. If it cannot, it does not run.

From the field · 18 Jul 2026

The Alignment Forum announced a $200,000 corrigibility research fund to advance the technical challenge of keeping advanced AI systems modifiable and shutdown-able as capabilities increase.

Corrigibility is moving from research concept to an operational requirement: security teams deploying agentic systems need to plan for controlled shutdown and behavioral override capabilities now.

What to learn next

Security architecture: design agent deployment architectures that include verified shutdown paths, human-in-the-loop overrides, and behavioral rollback mechanisms, and test these under adversarial conditions.

Source

From the field · 17 Jul 2026

MIT Technology Review reports that the shift toward AI-driven weather forecasting is creating new risks from coordinated sensor data manipulation, with demonstrated financial exploitation and potential for national-security-level sabotage.

AI systems that consume real-time observational data inherit the integrity properties of their data sources, and adversary incentives to manipulate those sources are growing.

What to learn next

Security monitoring: implement continuous integrity verification for observational and sensor data feeds consumed by AI decision pipelines, including anomaly detection, cross-source validation, and tamper-evident logging.

Source

From the practice · 17 Jul 2026

Forrester released the AEGIS playbook providing structured guardrails on AI agent intent, authority, and access so that adoption stays accountable, auditable, and defensible.

A vendor-neutral governance framework for agentic AI is now available, giving security teams a reference model to assess and constrain agent deployments before scaling.

What to learn next

Governance and risk management: adopt structured agent guardrail frameworks covering intent authorization, access scoping, and audit logging, and integrate them into the existing third-party risk and architecture review processes.

Source

Every item on Ace’s record