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We are sitting at the intersection of cybersecurity and artificial intelligence in the enterprise, and there is much to know and do. Our goal is not just to keep you updated with the latest AI, cybersecurity, and other crucial tech trends and breakthroughs that may matter to you, but also to feed your curiosity.
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Welcome to the first edition of our new format aimed at providing you more value:
Did You Know - Agentic AI Security Gaps
Strategic Brief - The Ungoverned Agent Problem
Threat Radar
The Toolkit
AI & Cybersecurity News & Bytes
C-Suite Signal
Byte-Sized fact
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💡 Did You Know - Medical Device Cybersecurity
Did you know that Gartner named agentic AI security the single most important cybersecurity trend for 2026, above ransomware, supply chain attacks, and identity threats?
Did you know that 75.4% of CISOs now consider AI agents a critical or significant security risk, yet most organizations have no formal governance framework for the agents already running in their environments?
Did you know that 30.4% of organizations experienced suspicious AI agent activity in 2025 meaning nearly one in three companies already had an AI agent behave in an unexpected or potentially malicious way, and most didn't know what to do about it?
Did you know that 99.4% of organizations experienced at least one SaaS or AI ecosystem security incident in 2025, according to a CISO survey cited in Gartner's report?
Did you know that the AI-amplified cybersecurity market is projected to reach $160 billion by 2029, up from $49 billion in 2025, a 3x expansion driven by enterprises rushing to secure the AI systems they rushed to deploy?
Did you know that over 75% of enterprises will use AI-amplified security products by 2028, but today fewer than 25% have them, as most organizations are in the gap between AI attack exposure and AI-powered defense?
🎯 STRATEGIC BRIEF:
The Ungoverned Agent Problem
Here's a thing that should keep every CISO up at night: your organization almost certainly has AI agents running right now that nobody in security has inventoried, governed, or monitored. Not because your team is sloppy. Because the agents got deployed faster than the governance frameworks exist to manage them.
That's the crisis Gartner is naming out loud. And it's real.
The Issue
AI agents are different from the AI tools we governed before. A ChatGPT integration that drafts emails is a tool — a human reviews the output, clicks send, takes responsibility. An AI agent that browses the web, reads your email, writes code, and executes API calls on your behalf is something else entirely. It acts. It makes decisions. It touches systems.
And right now, most of those agents operate with three dangerous characteristics: broad permissions they were given at setup and never reviewed, no audit log of what decisions they made or why, and no human in the loop for the actions that actually matter.
30.4% of organizations saw suspicious AI agent behavior in 2025. Think about what that means. Nearly one in three companies had an AI agent do something unexpected and execute an unintended action, access data it shouldn't have, behave differently than expected under new inputs. And the vast majority had no monitoring in place to catch it in real time.
The attack surface this creates is new. Adversaries aren't just trying to break into your network anymore. They're trying to manipulate your AI agents through prompt injection (feeding malicious instructions into the data the agent processes), through compromising the tools the agent uses, through poisoning the context the agent reasons over. If your agent has write access to your email, your CRM, or your cloud environment, an attacker who can manipulate that agent's inputs has everything they need.
The Opportunity
The security industry is building fast to meet this. A new category called "AI Security Posture Management" (AI-SPM) has emerged specifically to govern agentic AI in the enterprise, which is similar in concept to how Cloud Security Posture Management (CSPM) tools govern cloud environments.
Tools like Prompt Security, Protect AI, and Cranium are building platforms that inventory AI agents across an organization, monitor their behavior in runtime, and flag anomalies. Think of them as the SIEM for your AI layer catching what your agents are actually doing, not just what they were configured to do.
On the identity side, the emerging best practice is treating AI agents as non-human identities (NHIs) — assigning them unique credentials, scoping their permissions to exactly what they need, and rotating those credentials on a schedule. Just like you would a service account. Because that's what an agent is. It's a service account that reasons.
The governance frameworks are catching up too. NIST released draft guidance on agentic AI security in Q1 2026, and CISA is expected to follow with operational guidance before mid-year. Getting ahead of the framework now means you're not scrambling when compliance requirements land.
Why It Matters
The board conversation about AI used to be about ROI. That conversation is shifting. When 75.4% of your peer CISOs consider AI agents a critical risk, and Gartner puts it at the top of the threat landscape, the board question becomes: "Do we know what our AI agents are doing?" If you can't answer that, you have a governance gap to close before an agent closes it for you in a way you didn't authorize.
The Playbook:
Inventory Before You Govern: Run a full discovery of every AI agent deployed across your organization, sanctioned and shadow. You can't govern what you don't know exists. This includes copilots with autonomous action capabilities, AI agents embedded in SaaS products, and any custom-built agents your development teams deployed.
Treat Agents Like Identities: Assign every AI agent a unique non-human identity with scoped permissions, credential rotation, and an access log. The same zero-trust principles you apply to human users apply here. Least privilege, mandatory rotation, full audit trail.
Deploy Runtime Monitoring: Implement AI behavior monitoring that watches what your agents actually do in production, not just at configuration time. Flag actions outside the expected envelope. An agent that suddenly starts accessing data outside its normal scope is a signal, not a footnote.
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📡 THREAT RADAR - Rapid intelligence on active threats
Microsoft SharePoint CVE-2026-32201:
Risk: High — actively exploited zero-day, authentication spoofing
Impact: Attackers can impersonate authenticated SharePoint users and access documents, sites, and connected data without valid credentials. CISA confirmed active exploitation in the wild.
Action: Apply the April 2026 Patch Tuesday update immediately. CISA's federal remediation deadline is April 28. If you run SharePoint Server on-premises, this is your top patch priority this week.
Fortinet FortiClient EMS (CVE-2026-35616 + CVE-2026-21643):
Risk: Critical — pre-auth API bypass and SQL injection, both under active exploitation
Impact: An unauthenticated attacker can bypass API access controls and execute arbitrary database commands against FortiClient EMS — widely deployed enterprise endpoint security management infrastructure.
Action: Patch FortiClient EMS to the latest release now. The federal CISA deadline for CVE-2026-35616 was April 9. If you're running an unpatched version, assume active targeting. Run watchTowr's published detection script to check for compromise indicators.
SAP SQL Injection CVE-2026-27681:
Risk: Critical — CVSS 9.9, unauthenticated remote code execution
Impact: A low-privileged user can send crafted HTTP requests to execute arbitrary database commands in affected SAP environments — giving attackers a path to financial data, HR records, and core ERP systems.
Action: Apply SAP's April 2026 security patch bundle immediately. This vulnerability is rated 9.9 — it doesn't get more critical. Prioritize SAP patching even if it requires a maintenance window this week.
🛠️ THE TOOLKIT - Solutions for the Post-MFA Era
The Inventory: Cranium AI
Problem: You don't have a complete list of AI agents running in your environment, which means you can't govern what you haven't found.
Solution: Cranium discovers and inventories AI models and agents across your organization's infrastructure, giving you the visibility foundation that governance requires.
The Monitor: Protect AI
Problem: AI agents behave at runtime in ways that weren't anticipated at configuration, and most organizations have no monitoring in place to catch anomalies.
Solution: Protect AI's platform monitors AI model behavior in production, detects prompt injection attempts, and flags agents operating outside their expected behavioral envelope.
The Identity Layer: CyberArk Conjur (for NHI governance)
Problem: AI agents often run with over-broad, static credentials that were set at deployment and never reviewed — the machine identity equivalent of a shared admin password.
Solution: CyberArk's secrets management platform assigns AI agents scoped, short-lived credentials with automated rotation and full audit logging — closing the identity gap that agentic AI creates.
Artificial Intelligence News & Bytes 🧠
Cybersecurity News & Bytes 🛡️
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📊 C-SUITE SIGNAL - Key talking points for leadership
Agentic AI Is Now a Board-Level Security Risk: Gartner's identification of agentic AI security as the top cybersecurity trend for 2026 gives CISOs the external validation needed to elevate this conversation in the boardroom. The question isn't whether your organization uses AI agents, it's whether you know what they're authorized to do, what they're actually doing, and who's accountable when one of them acts outside those bounds.
The Liability Gap Is Widening: 62% of CISOs cite security concerns as the primary blocker for scaling agentic AI, yet business units are deploying agents regardless. That creates a gap with legal and operational liability for agent behavior that security teams haven't been resourced to govern. CISOs need to formalize AI agent ownership the same way they formalized cloud ownership: who deployed it, who owns the risk, who gets the call at 2am when it goes wrong.
🧠 BYTE-SIZED FACT
In the early days of nuclear power, the U.S. Atomic Energy Commission licensed reactors and assumed operators would follow safety protocols as written. Nobody built real-time monitoring into the original regulatory framework. Then Three Mile Island happened in 1979, and the investigation found that operators had made a series of individually reasonable decisions that collectively produced a disaster. The monitoring gap was the problem, not any single decision.
The Lesson: Powerful systems operating without real-time behavioral monitoring tend to produce surprising outcomes. AI agents with broad permissions and no runtime oversight are today's version of that gap. We already know how these stories end.
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