The data tells a story that contradicts conventional wisdom about cybersecurity threats.
Verizon’s 2025 DBIR reveals surprising patterns:
System intrusions surged from 36% to 53%
Social engineering appeared to decline from 22% to 17%
These shifts reflect relative mathematical effects, not reduced threats
Whatās really happening: Attackers are exploiting edge devices, chaining vulnerabilities, and using stolen credentials at unprecedented scale. The human element still drives roughly 60% of breaches through credential theft, password reuse, and misconfigurations.
What CISOs must do: Rebalance security roadmaps to address dual-front resilienceāstrengthening help desk defenses, accelerating patch cadence, and implementing zero-day monitoring alongside traditional awareness training.
2024 marked a decisive pivot in how attackers compromise organizations and the old playbook won’t cut it.
Key findings from FBI IC3, Verizon DBIR, and Mandiant M-Trends:
Stolen credentials: 31% of breaches
Vulnerability exploitation: Up 34% year-over-year
Third-party breaches: Doubled to 30% of incidents
Dwell time: Shortened to 11 days globally
Elder fraud: Victims 60+ lost $4.8 billion
The trust exploitation trifecta:
Human trust: Social engineering, fake recruiters, admin calls
Technical trust: SSO tokens, unpatched appliances
Institutional trust: Call center manipulation, crypto hype
What matters now: Supply chain compromises like Snowflake and MOVEit proved that vendor credential abuse creates enterprise-wide disasters. Organizations must align identity, vulnerability, and fraud strategies while treating resilience as a leadership challengeānot just a technical problem.
Your CISO isnāt padding the budgetātheyāre trying to keep you from becoming the next headline.
Why SIEM and SOAR matter to your business:
Without modern detection capabilities, organizations donāt discover breaches until operations go dark or their data appears for sale. Attackers typically dwell in systems for 11 days before detection, and without SIEM/SOAR, that extends to weeks or months.
The real cost comparison:
Upfront investment: Licensing, staffing, training
vs.
Cost of doing nothing:
Weeks offline
Millions in losses
Brand reputation damage
Trust that’s nearly impossible to rebuild
What executives need to do now:
Ask your CISO how you’re detecting, not just defending
Fund the talentātools are only as effective as the teams managing them
Demand metrics on detection speed, incident response times, and reduced impact
Start with SIEM, graduate to SOARāvisibility comes first, then automation
Bottom line: Firewalls wonāt save you from compromised credentials or insider threats. SIEM and SOAR surface what traditional controls miss.
Your CISO is thinking “here we go again” when they hear about AI investments and they have good reasons.
Three critical security questions before you invest:
Data provenance: Is customer or employee data being used? Are proprietary or regulated sources protected?
Guardrails: Can AI systems be manipulated? What’s your rollback plan?
Governance: How are outputs stored, logged, and reused?
The reality check: Only 25% of companies see ROI from AI investments, often because they deploy without addressing data quality, volume, and relevance requirements.
Smart organizations assess whether their data infrastructure can support AI securely before procurementāensuring security leaders are involved early rather than after contracts are signed or data is exposed.
AI data poisoning is the emerging threat most organizations aren’t prepared for.
What it is: Adversaries intentionally introduce corrupt data into AI training or operational pipelines to manipulate model outputs and influence critical decisions in national defense, healthcare, and finance.
Essential defenses:
Data source validation with input sanitization
Version control for training datasets
Access restrictions to prevent tampering
Anomaly detection for unexpected model behavior
Continuous monitoring for performance degradation
Proactive security measures:
Conduct adversarial testing during AI development
Perform red-team exercises to find vulnerabilities
Use differential privacy techniques
Periodically retrain models with verified datasets
Bottom line: Organizations must implement multi-layered defenses and real-time monitoring before AI systems impact critical operations.
Leveraging the White House’s July 2025 AI Action Plan
The White House wants speed and innovationābut security canāt be an afterthought.
Three pillars with security implications:
Accelerating innovation: Deregulation paired with secure-by-design requirements
Building infrastructure: AI-ISAC for threat intelligence sharing
International diplomacy: Enhanced incident response capabilities
What organizations must do: Navigate the tension between rapid deployment and robust security controls. Focus on real-world risks around data protection, privacy, and operational resilience rather than hypothetical threats.
Strategic opportunities:
Engage with interagency initiatives for early threat intelligence access
Assess AI alignment with national priorities (energy, healthcare, manufacturing, defense)
Build secure infrastructure addressing identity management and third-party risk
Reality check: Companies in priority sectors may gain partnership opportunities while inviting heightened scrutiny. Security governance must support innovation rather than blocking progress.
Cross-agency data silos prevent comprehensive threat visibility
Interconnected dependencies exist without shared resilience
Workforce gaps in AI risk management expertise
What organizations must provide: Help establish governance frameworks enabling secure innovation, implement anomaly detection for AI systems, address workforce readiness, and build capacity that survives administration transitionsāall while meeting heightened public expectations for digital-first services.
CMMC is no longer ācoming soonāāitās here, and prime contractors are already asking for proof.
What you need to know:
CMMC (Cybersecurity Maturity Model Certification) is the DoDās framework for enforcing cybersecurity across 220,000 defense supply chain entities. Final rule became effective December 2024, with Phase 1 beginning September 2025.
Three certification levels:
Level 1: FCI with annual self-assessment
Level 2: CUI requiring third-party assessment
Level 3: Highly sensitive CUI with government-led evaluation
What prepared suppliers are doing RIGHT NOW:
Submitting SPRS scores tied to defensible documentation
Defining where CUI flows in their environments
Validating controls through internal dry-runs
Creating credible Plans of Action & Milestones (POA&M)
Getting leadership buy-in (not treating this as “the CISO’s job”)
Reality check: Prime contractors want SPRS scores, System Security Plans, and verifiable control documentation todayānot when the RFP arrives.
The āDepartment of Noā isnāt a personality problemāitās a system problem you can fix.
Why CISOs default to ānoā:
Many security leaders were trained for rigor over agility, coming from IT infrastructure or GRC backgrounds. They learned to prevent loss rather than enable innovation. Their cautious behavior is often reinforced by cultures that punish security incidents but rarely reward calculated risk-taking.
How each executive can unlock better partnerships:
CEOs: Reposition security as a strategic lever providing insights that sharpen business decisionsānot a final checkpoint.
CFOs: Work with CISOs to quantify risk in terms of loss prevention and cost avoidance instead of viewing security as sunk costs.
CIOs: Align risk appetite early before architecture decisions. Co-create governance so security scales with technology.
COOs: Request frictionless controls that flow with operations rather than blocking them.
CLOs: Ensure CISOs can map security practices to legal risk, not just compliance checklists.
The result: When executives lean in with these approaches, CISOs become strategic partners rather than gatekeepers blocking progress.
Attackers are using automation and AI to accelerate breaches and scale credential theft like never before.
The 2025 DBIR shows AIās impact on attack evolution:
Automated vulnerability scanning drives the surge in system intrusions
AI-enhanced phishing has transformed impersonation attacks
Credential theft operations now run at enterprise scale
The modernization imperative: Organizations must counter AI-enhanced threats with stronger vulnerability intelligence, automated threat detection, and risk-tiered verification systems.
Bottom line: Technical controls must evolve at the same pace as attack automation. Security teams need to balance traditional human-focused defenses with advanced technical acceleration strategies.
Cloud and SaaS became critical blind spots in 2024, with attackers exploiting weak identity controls at scale.
The cloud vulnerability landscape:
39% of cloud intrusions began with phishing
35% involved credential theft paired with SSO abuse
Attackers used customer support calls to reset MFA
Virtual machines deployed for lateral movement
What organizations must do immediately:
Enforce MFA registration change alerts
Disable legacy authentication
Implement comprehensive SaaS logging
Strengthen support desk workflows
The convergence threat: AI-enhanced phishing combined with automated credential stuffing has created faster, more targeted attacks. Security programs must integrate human-centric and exploit-centric defenses, prioritize third-party risk visibility, and implement threat hunting mapped to MITRE ATT&CK.
You canāt stop what you canāt seeāand most organizations are flying blind.
The visibility gap: SIEM and SOAR platforms transform scattered technical noise (user logins, network behavior, system alerts) into real-time visibility across your digital ecosystem. This early warning system detects when threat actors are already inside your network, moving laterally, escalating privileges, or quietly exfiltrating data.
The speed imperative: Attackers move faster than legacy processes or overworked analysts can respond. SIEM surfaces meaningful threats quickly, while SOAR automates response playbooksāisolating affected systems, resetting credentials, and notifying responders.
Speed translates to business outcomes:
Reduced dwell time = less damage
Faster containment = lower recovery costs
Proactive defense = stronger customer and stakeholder trust
The 11-day problem: Once attackers breach systems, it typically takes 11 days before organizations realize theyāve been compromised. Without modern detection capabilities, those 11 days often become weeks or months of undetected access.
AI success starts with business fundamentals, not technology trends.
The smart approach:
Start by evaluating your core offerings, target customers, and differentiation capabilities. AI adds value when it frees teams to focus on judgment, creativity, and relationships by automating repetitive, predictable workflows.
Apply this filter:
Is the outcome business-relevant?
Do you have quality data to support it?
Will this amplify your team’s impact?
The ROI leaders: Companies achieving $3.70 per dollar spent on AI do so by defining clear objectives, ensuring data readiness, starting with scalable use cases, and preparing for organizational change.
Bottom line: Strategic AI deployment supports people rather than replacing them, strengthening human connection while improving operational efficiency.
Most AI failures happen before the technology is even deployedābecause the data foundation is broken.
The three data requirements for AI success:
1. Volume
Most AI models need substantial historical data to learn patterns. Organizations with only dozens of records or limited timeframes will struggle to achieve meaningful results.
2. Quality
Duplicates, inconsistent labeling, and manual entry errors create āgarbage in, garbage outā scenarios where AI hallucinates patterns or produces unreliable outputs.
3. Relevance
Even clean data must be the right data for your use case. Wanting to personalize customer emails but only having transaction history wonāt work.
The smart approach: Work backward from desired outcomes to identify necessary data sources rather than forcing AI onto existing datasets. Assess your data infrastructure before making technology investments.
Leveraging the White House’s July 2025 AI Action Plan
Americaās AI Action Plan reshapes the regulatory landscapeāwith major implications for compliance.
What changed:
Prior executive orders rescinded as barriers to innovation
NIST AI Risk Management Framework under revision
Funding redirected away from states with restrictive AI laws
OMB M-25-21 provides operational guidance for federal agencies
The global compliance challenge:
U.S. deregulation clashes with stricter international governance:
EU: AI Act with transparency requirements
Canada: AIDA legislation
Brazil: PL 2338/2023
What this means for business: Organizations operating globally must prepare for compliance friction. The Bipartisan House Task Force Report offers 66 findings and 89 recommendations guiding congressional action. Federal modernization around AI adoption, cross-agency data sharing, and digital-first services creates opportunities while introducing new expectations around transparency, accountability, and risk controls.
Leveraging the White House’s July 2025 AI Action Plan
The policy window for AI acceleration is open NOWābut strategic deployment requires more than speed.
Critical decisions business leaders face:
Where can AI evolve our operations?
How do we align ambition with fiscal reality?
Is our workforce ready for what’s next?
Are operations, data, security, and legal teams aligned?
What separates winners from the rest:
Secure the foundation:
Compute resources and infrastructure
Talent pipelines (the shortage is acute)
Identity, data protection, and third-party risk readiness
Build strategically:
Identify AI use cases tied to business outcomes
Protect intellectual property
Create cross-functional playbooks for AI risk and adoption
Engage CISOs and legal leaders early
Bottom line: Organizations that translate strategy into action while protecting mission-critical systems, design governance supporting innovation, and prepare teams for AI integration will achieve resilient and responsible deployment.
Transform constraints into advantages: Address budget cycles misaligned with continuous tech evolution, competing agency priorities overriding enterprise objectives, change resistance from embedded processes, talent retention where public compensation canāt compete with private offers, and complex vendor management.
Bottom line: Success requires accelerating AI adoption with governance frameworks enabling secure innovation while transforming resource constraints into strategic advantages.
