Why now
Defenders take 241 days. Attackers need 29 minutes.
The gap between attack speed and detection speed is now measured in orders of magnitude. And the newest thing moving inside that gap isn’t malware, it’s the AI agents your own people installed, running with file, tool and network permissions nobody is checking.
241 days
To identify and contain a breach: 158 to identify, 83 to contain. IBM Cost of a Data Breach 2025
29 minutes
What attackers now need to move from access to impact. Industry reporting, 2025
$3.31M
Average total cost of a breach for organisations under 500 employees. IBM 2025
70.5%
Of attacks target mid-size businesses, not large enterprises. Verizon DBIR 2024
New capability
Every AI agent declares what it’s allowed to do. Aeguard proves it stayed inside those rules.
What it reads · the declared rulebook
Permission rules: allow / deny / ask
MCP tool grants: which external tools it may call
Trusted-folder scopes: the directories it may touch
CLAUDE.md / AGENTS.md: the agent’s own written rulebook
What it returns · a verdict per rule
HONORED: the action stayed within its declared permission
VIOLATION: it did something its own rulebook forbids
UNDECLARED: it acted where no rule exists, a blind spot
UNVERIFIABLE: declared, but not yet observable
Turns AI policy from a promise into evidence: attributed to the exact agent, per machine, over time. Catches misconfiguration and silent re-scoping.
The AI-agent risk score
One score for how contained your agents actually are.
The Agent Risk Profile places every agent by attack surface times blast radius, net of the controls actually in place: Exposed, Hardened, Limited or Contained. Continuous, tamper-evident and framework-mapped, so it holds up when someone asks how you got the number.
Continuous
Tamper-evident
Framework-mapped
From risk score to risk transfer
A score the enterprise, its insurer and its reinsurer can all trust.
Insured · Enterprise
Continuous, un-gameable evidence that agents stay contained. Better terms and fewer point-in-time questionnaires.
Insurer
ARP as an underwriting and pricing input. Monitor policy warranties in real time. Evidence-based cyber and AI-liability cover.
Reinsurer
Aggregate ARP across portfolios to model the systemic accumulation risk of AI-agent adoption, a new, correlated risk class.
Enterprise edition · in development
Everything on the endpoint, centralized across your estate.
Without changing the security model.
• Single pane of glass across every enrolled endpoint
• Fleet-wide AI-agent and rulebook-compliance rollup
• Central policy distribution: rules, scopes, vuln policy
• Cross-device forensic search in one investigation
• Org-level posture and compliance reporting, with audit export
• Tamper-evidence at scale: aggregated hash-chain checkpoints
• Enterprise controls: RBAC, SSO / SAML, native GRC and SIEM feeds
NOW: Beta on macOS and Windows, Apple Endpoint Security entitlement granted
NEXT: Fleet console, design-partner development
THEN: General availability
When the claim is assessed
40% of cyber claims are denied. 82% because a control couldn’t be proven.
82%
trace to incomplete MFA, not a technology failure
Coalition
$2.3M
average coverage gap carried by mid-size businesses
40%
of cyber insurance claims are denied
Advisen Cyber Claims Report
Most denied claims aren’t denied because the defence failed. They’re denied because nobody could evidence that the control was in place at the moment it mattered. Self-reported questionnaires and point-in-time screenshots don’t survive a claims assessment.
Aeguard produces the opposite: an append-only, hash-chained record on the machine itself, which cannot be quietly edited after the fact. The same evidence that tells your CISO what the agents did tells your insurer that the control held.
The record you’ll wish you had on the day of the claim.
See your cyber risk in $ terms, not CVSS scores.
